Compressed Sensing High Resolution Functional Magnetic Resonance Imaging

ABSTRACT

The present disclosure provides methods and systems for high-resolution functional magnetic resonance imaging (fMRI), including real-time high-resolution functional MRI methods and systems.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority pursuant to 35 U.S.C. § 119(e) to thefiling date of U.S. Provisional Application No. 62/212,335, filed Aug.31, 2015, the disclosure of which is incorporated herein by reference.

INTRODUCTION

Functional magnetic resonance imaging (fMRI) has been used inneuroscience research and for clinical applications. However, achievinghigh spatial resolution remains a significant challenge in fMRI becauseof a trade-off with decreased temporal resolution and/or lowercontrast-to-noise ratio (CNR). High magnetic field systems, improvementof coil sensitivity, advancements in pulse sequences, utilization ofparallel imaging, and reconstruction with partial k-space have been usedto try to increase spatial resolution. However, demands for even higherspatial resolution are common.

SUMMARY

The present disclosure provides methods and systems for high-resolutionfunctional magnetic resonance imaging (fMRI), including real-timehigh-resolution fMRI methods and systems.

Aspects of the present disclosure include a method for functionalmagnetic resonance imaging (fMRI) of a subject. The method includesapplying with an MRI system, a balanced steady state free precession(b-SSFP) sequence to a target area in a subject, acquiring with the MRIsystem, image data of the target area in the subject using a randomlyundersampled variable density spiral (VDS) trajectory, and producing animage of the target area in the subject based on the acquired imagedata.

In some embodiments, the producing comprises analyzing the image datausing a spatial sparsifying transform. In some embodiments, the spatialsparsifying transform comprises a discrete cosine transform (DCT).

In some embodiments, the method is a real-time fMRI method. In someembodiments, the producing comprises analyzing the image data using afast iterative shrinkage thresholding algorithm (FISTA).

In some embodiments, the method has a sampling acceleration factor of 2or more. In some embodiments, the method has a sampling accelerationfactor of 5 or more.

In some embodiments, the method produces an image having a spatialresolution of about 0.2×0.2×0.5 mm³ or greater.

In some embodiments, the method produces an image having acontrast-to-noise ratio of 1.5 or more. In some embodiments, the methodproduces an image having a contrast-to-noise ratio of 2.5 or more.

Aspects of the present disclosure include a functional magneticresonance imaging (fMRI) system. The system includes a coil configuredto apply a balanced steady state free precession (b-SSFP) sequence to atarget area in a subject, a receiver configured to acquire image data ofthe target area in the subject using a randomly undersampled variabledensity spiral (VDS) trajectory, and a processor configured to producean image of the target area in the subject based on the acquired imagedata.

In some embodiments, the processor is configured to analyze the imagedata using a spatial sparsifying transform. In some embodiments, thespatial sparsifying transform comprises a discrete cosine transform(DCT).

In some embodiments, the system is configured for real-time fMRI. Insome embodiments, the processor is configured to analyze the image datausing a fast iterative shrinkage thresholding algorithm (FISTA).

In some embodiments, the system has a sampling acceleration factor of 2or more. In some embodiments, the system has a sampling accelerationfactor of 5 or more.

In some embodiments, the processor produces an image having a spatialresolution of about 0.2×0.2×0.5 mm³ or greater.

In some embodiments, the processor produces an image having acontrast-to-noise ratio of 1.5 or more. In some embodiments, theprocessor produces an image having a contrast-to-noise ratio of 2.5 ormore.

BRIEF DESCRIPTION OF THE DRAWINGS

A randomized, variable-density, under-sampled spiral acquisition schemewas designed for the HSPARSE fMRI. (FIG. 1A) A low spatial resolutionNyquist trajectory only covers a small range of k-space. (FIG. 1B) Toachieve higher spatial resolution without introducing aliasing artifactsor changing the field of view, the k-space coverage can be increasedwith more interleaves. This inevitably increases the data acquisitiontime and reduces the temporal resolution. (FIG. 1C, 1D) To overcome thisproblem, a variable density spiral (VDS) trajectory was designed andinterleaves were randomly sampled while keeping the total number ofinterleaves and scan time the same as the low spatial resolution Nyquistscan. (FIG. 1E, 1F) For 3D acquisition, the HSPARSE fMRI method randomlyselects 320 interleaves from a stack of VDS trajectory consisting 32 kzlocations and 30 interleaves. More interleaves were sampled near thek-space center and the total number of interleaves in each kz locationfollows a Laplacian distribution. The sampling pattern was also chosento be random across temporal frames to exploit the temporal sparsity.However, the total number of interleaves for each time frame wasdesigned to be constant (320 interleaves) to maintain constant temporalresolution over time. Compared to a 3D Nyquist sampled trajectory thathas the same spatial resolution, the trajectory used herein achieved ahigh acceleration factor of 5.3.

Separating the spatial and temporal sparsity constraints resulted inhigher image contrast, HRF amplitude, and allowed lower noise level andfalse positive rate. (FIG. 2A-D) The optimized 4D DCT based compressedsensing (CS) reconstruction with regularization parameter of λ=1e⁻² wasfirst compared with the 3D+1D DCT based method using a phantom. Thereconstruction using 3D+1D DCT produced higher mean F-value, imagecontrast and lower noise level compared to the reconstruction using 4DDCT. Although slightly higher sensitivity was observed in thereconstruction using 4D DCT, the false positive rate of the 4D DCT basedmethod was also much larger than the 3D+1D based method. Furthermore,the reconstruction using 4D DCT also resulted in smoother and loweramplitude HRFs compared to the HRFs reconstructed with the 3D+1D DCT.(FIG. 2E, 2F) Similarly, the 3D+1D method produced higher mean F-value,contrast and lower noise level in an in vivo dataset. (FIG. 2G) Thereconstruction using 3D+1D DCT also allowed a higher HRF amplitude,indicating the 3D+1D DCT regularization resulted in less temporaldistortion.

Design of the GPU accelerated CS reconstruction algorithm. (FIG. 3A) Keycomputationally intensive calculations such as the NUFFT, matrixarithmetic, and DCT were parallelized on a GPU. Since these computationswere repeatedly used during the iterative reconstruction loops used inHSPARSE, the GPU parallelization significantly improves thereconstruction speed. The iNUFFT and NUFFT were the most complicated andtime-consuming calculations in the HSPRSE reconstruction. (FIG. 3B)iNUFFT resamples the gray Cartesian grid onto the blue spiral samples.In the parallel implementation, each GPU core was assigned a spiralsample, and each thread inside the GPU core was assigned a Cartesiangrid within the corresponding spiral sample's convolution window. Duringthe iNUFFT, each thread first calculates its Cartesian grid'scontribution to the given spiral sample, then an efficient binarysummation algorithm was performed to sum all values together. (FIG. 3C)NUFFT resamples the blue spiral samples back onto the Cartesian grids.In contrast to the iNUFFT, each GPU core was assigned to a spiral sampleat a different kz-location to avoid memory write conflict. Each threadinside the core then retrieves value from the spiral sample point andadds it to the corresponding Cartesian grid inside the convolutionwindow. Because there were thousands of kz slices in the 4D fMRIdatasets, this NUFFT algorithm took full advantage of the massive numberof GPU cores.

FIG. 4: Pre-computation of line search decompositions improved thecomputational efficiency of the gradient descent method.

Phantoms used for optimization and testing of HSPARSE fMRI. (FIG. 5A) Toidentify the optimal regularization parameters for reconstruction, threephantoms with two noise levels (25 dB and 30 dB) were generated. Thephantoms were designed to first simulate an in vivo fMRI experiment(A1), then to assess the effects of having a distinct base image (B1)and a distinct activation pattern (C1). To simulate more realisticimaging cases, the activation patterns were designed to have decreasingamplitude towards the edge of the activation through Gaussian smoothing(see methods). (FIG. 5B) For the assessment of fMRI signal sensitivityand specificity, three 30 dB phantoms were designed to have sharpactivation boundaries with activation patterns limited to a singleslice. (FIG. 5C) To investigate temporal characteristics of thereconstructed HRFs, three additional 30 dB phantoms without Gaussiansmoothing were generated that simulated an in vivo fMRI experiment (A3),an experiment with a different base image (B3), and an experiment with adistinct activation pattern (C3).

HSPARSE fMRI method achieved high signal sensitivity and low falsepositive rate across a wide range of CNRs and phantoms. (FIG. 6A) ThefMRI signal sensitivity was defined as the number of true positive (TP)voxels over the number of true positive and false negative (FN) voxels.(FIG. 6B) The false positive rate was defined as the number of falsepositive (FP) voxels over the number of false positive and true negative(TN) voxels within the 1- to 5-pixel perimeter layers of the designedactivation volume (FPR1 to FPR5). (FIG. 6C) Example reconstructions ofthe A2-C2 phantoms with 10% peak HRF amplitude show that thereconstructed fMRI signals were mainly confined to the designed activearea with limited false positive activations outside. (FIG. 6D)Sensitivity and FPR tests were performed on the A2-C2 phantoms with fourdifferent peak HRF amplitude of 10-4% (corresponding to CNR of2.55-1.23). The mean sensitivities of the HSPARSE reconstructed datasetswere found to be 69 to 99% of the original sensitivities with noise forall tested peak HRF amplitudes. (FIG. 6E) The mean false positive rateswere less than 0.051 on the 1-pixel perimeter layer and less than 0.01on the 2-pixel perimeter layer. Error bar represents standard erroracross the A2-C2 phantoms. Taken together, these data show that theoptimal HSPARSE reconstruction results in high sensitivity and low FPR.

HSPARSE fMRI method resolved spatially adjacent yet functionallydistinct regions. (FIG. 7A, 7D) A rat brain phantom with three layers ofdistinct peak HRF amplitude/latency in the cortex was designed. HRFs ofthe three layers and their corresponding principal componentdecompositions demonstrate clear separation of the three layers. Thickerlines in the HRF plot represent the mean HRF of each layer. (FIG. 7B,7E) Highest spatial resolution Nyquist acquisition resulted in activitywith obscured boundaries between layers. (FIG. 7C, 7F) HSPARSEreconstruction correctly identified the spatial location where theamplitude/time-to-peak transition occurs.

Resolution limit of the HSPARSE fMRI was identified. (FIG. 8A, 8D) Aphantom that was challenging to reconstruct with low spatial resolutionwas designed with an interleaved pattern consisting of distinct peak HRFamplitude/latency features. (FIG. 8B, 8E) The highest spatial resolutionNyquist acquisition completely failed to separate the six interleavedlayers. (FIG. 8C, 8F) In contrast, the HSPARSE reconstructionsuccessfully resolved the peak HRF amplitude and latency differences forall six layers. (FIG. 8G, 8H) To identify the resolution limit of theHSPARSE method, a phantom with interleaved activation layers of variablethickness (4-1 pixels or 0.84˜0.21 mm) was designed. Layers withdistinct peak HRF amplitude/latency can be distinguished down to3-pixels (0.62 mm) using the Nyquist acquisition and down to a singlepixel (0.21 mm) using the HSPARSE method.

HSPARSE fMRI method resolved in vivo layer-specific activity evoked byoptogenetic stimulation of dentate gyrus. (FIG. 9A) Schematic showingoptogenetic targeting of the dentate gyrus. (FIG. 9B) Dentate gyrus hada unique horn shape, with its coronal and axial slices showing “0” and“U” shaped profiles, respectively. (FIG. 9C) Histological examinationverified that the ChR2-EYFP expression was localized to the dentategyrus region. (FIG. 9D) The Nyquist acquisition failed to accuratelylocalize dentate gyrus activity and activity occurs on both the dentategyrus and the CAL In contrast, both the original and the three timesaveraged HSPARSE fMRI showed activity localized to the dentate gryus,with the voxels having high peak HRF amplitude precisely following thegeometry of the structure's molecular layer. White triangles in the toprow indicate the approximate site of stimulation. Active voxels wereidentified as those having an F-value greater than 4.42 (p<0.001). Theactive voxels' peak HRF amplitudes were then calculated and overlaidonto a high-resolution MRI atlas, with a threshold at the median plus1.5 times the standard deviation of all peak HRF amplitudes for clearvisualization with good dynamic range. (FIG. 9E, 9F) As was seen withthe simulations, the HSPARSE-reconstructed HRF amplitudes at thestimulation site were lower than their respective low-resolution scans.However, the HRFs were strongly correlated between the HSPARSEreconstructed and Nyquist sampled images, which indicated that in vivoHSPARSE fMRI maintained the temporal characteristics of in vivo HRFs.

Optimal range of CS regularization parameters were identified based onquantitative assessments of the reconstructed images. (FIG. 10A) Examplereconstructed images of the A1 phantom (30 dB) with differentregularization parameters. Voxels were considered to be active if theyexhibit an F-value greater than 4.42 (P<0.001). Lower left plot showsthe original ground-truth image. (FIG. 10B) An optimal regularizationparameter range was defined as the region that achieved higher CNR andmaximum correlation coefficient, larger active volume within thedesigned active region compared to the original ground-truth image, andNRMSE of less than 105% of the minimum NRMSE found within the searchrange. A range of regularization parameters were identified to yieldhigh reconstruction quality (lower right plot, blue area) for the 30 dBA1 phantom. The symbols ‘Â’ and ‘v’ in each plot indicate the maximumand minimum values in the corresponding test, respectively. “N/A”indicates an area in which CNR, maximum correlation coefficient, andpeak HRF amplitude cannot be computed due to limited activation. 1.02v,1.05v and 1.15v indicate the contour lines of 1.02, 1.05, and 1.15 timesthe minimum NRMSE value. (FIG. 10C) The optimal ranges for 6 phantomswith different base images, activation patterns and SNRs were overlaid,where a set of regularization parameters were found to provide optimalreconstruction quality for all phantoms tested. (FIG. 10D-H) With a setof parameters (=5e−3 and =1e−4) selected from the optimal range, CNR,the active volume within the original ground truth active region, andthe maximum correlation coefficient of the reconstructed image werehigher than the original phantom with noise, and the NRMSE was less than0.24 across all phantoms. Although the HRF amplitudes decrease after theCS fMRI reconstruction, the CS fMRI was shown to maintain the relativeamplitudes and shapes of HRFs in FIG. 11. (FIG. 10I) FWHM of the pointspread function was 0.70 mm with the highest spatial resolution Nyquistacquisition while it reduced to 0.32 mm for HSPARSE reconstructed imageswith optimal regularization parameters. The error bar shows the standarddeviation of the CS point spread function across temporal frames.

HSPARSE reconstruction using optimal regularization parametersmaintained HRF temporal characteristics over a range of physiologicallyrelevant HRF amplitudes. (FIG. 11A) Representative images of thereconstructed A3 phantom were shown with original HRF amplitudes of 4,6, 8, and 10%. All phantoms were reconstructed 5 times with independentidentical Gaussian distributed noise using regularization parameters(J=5e-3 and 21=1e−4) within the optimal range. (FIG. 11B) Although theHSPARSE reconstructed HRFs exhibit lower amplitudes than the originalHRFs for all tested amplitudes, the HRF shapes were similar afteramplitude normalization (inset on upper right). Error bars representstandard deviation across 5 reconstructions. (FIG. 11C) The correlationanalysis indicated that the HSPARSE reconstructed HRF time courses hadstrong linear correlation to the original HRFs (slope=0.52, R2=0.98).(FIG. 11D) After the amplitude normalization of all HRFs, the maximummean difference between the HSPARSE reconstructed and the original HRFswas less than 0.016 and the limits of agreement (±1.96×standarddeviation) were less than 0.080. (FIG. 11E, 11F) Similarly, thedurations of the HSPARSE reconstructed HRFs were not significantlydifferent from the original HRFs (P>0.22, Wilcoxon ranksum test), andmaximum time-to-peak differences (2.40s) was smaller than the 3 stemporal resolution of the fMRI acquisition. Similar results were seenin B3 and C3 phantoms.

HSPARSE fMRI method was robust against real physiological noise. (FIG.12A) The HSPARSE fMRI also improved the CNR, maximum correlationcoefficient, and active volume compared to their correspondingfully-sampled datasets in the presence of real physiological noise. TheNRMSE values were less than 0.081 across all subjects. Error barsrepresent the standard error across voxels of the active area for CNRand maximum correlation coefficient. (FIG. 12B, 12C) The imagesreconstructed with HSPARSE detect the majority of the activity and theactive voxels shared between the HSPARSE reconstructions and thefully-sampled images consist 90.3 to 93.0% of active voxels from thefully-sampled images. Because the ground-truth active region was notavailable for the in vivo experiment, additional active voxels onlydetected with HSPARSE reconstruction could either be false positivesignal or a result of improved sensitivity due to CNR increase. However,active voxels only detected with HSPARSE reconstruction was limitedwithin the 1-pixel perimeter layers of the active volume detected withthe fully-sampled dataset. (FIG. 12D) HRF amplitude reduction fromHSPARSE reconstruction has a scaling factor ranging from 0.40 to 0.48.Importantly, the HRFs from the fully-sampled datasets and the HSPARSEreconstructions had a strong linear correlation with a minimumcorrelation coefficient (R2) of 0.98, demonstrating that HSPARSEreconstruction maintains the temporal characteristics of thefully-sampled HRFs.

FIG. 13: Comparison of temporal HRF characteristics between the originalfully-sampled and

HSPARSE images in the presence of physiological noise. For all threesubjects, the HRF durations were similar and the maximum durationdifference was 1.67 s. The first subjects gave the same time-to-peak.The rest two subjects showed an increase in time-to-peak for the HSPARSEreconstructed image, but the difference was smaller than the 3 stemporal resolution of the acquisition.

Six-cycle time-series and analysis results corresponding to the mainFigures. (FIG. 14A, 14B) Six-cycle time-series corresponding to FIGS. 11and 12. Similar to the analysis performed on the HRFs, the HSPARSEreconstructed six-cycle time-series also show a strong linearcorrelation with the original ground-truth time-series, which indicatesthe HSPARSE method maintains high temporal fidelity. (FIG. 14C, 14D)Six-cycle time-series corresponding to FIG. 7 and FIG. 8. While somesinusoidal variations in the HSPARSE fMRI reconstructed time-series wereobserved (bottom left plot for both C and D), the HSPARSE fMRI was foundto preserve the peak amplitude and latency differences between layers,while the highest spatial resolution Nyquist acquisitions fail. HSPARSEalso maintains high sensitivity and low FPR. In contrast, Nyquistacquisitions result in high FPR, which could be the result of lowspatial resolution induced partial volume effects. (FIG. 14E) Six-cycletime-series corresponding to FIG. 9. In vivo acquired HSPARSE fMRIsix-cycle time-series also showed strong linear correlation with thetime-series obtained from the highest spatial resolution Nyquistacquisitions for all three subjects, demonstrating that HSPARSE fMRI canprovide high temporal fidelity for in vivo experiments.

FIG. 15A, 15B: Optimized HSPARSE fMRI method consistently resolvedlayer-specific activity of the dentate gyrus upon optogeneticstimulation. Two additional in vivo experiment results were shown. Withthe highest spatial resolution Nyquist rate sampled images, activity wasobserved throughout the hippocampus. In contrast, activities in theHSPARSE reconstructed images were confined to the dentate gyrus. Thepeak amplitude activities followed the geometry of the molecular layerfor all three subjects. The pink area and the red lines delineate thedentate gyrus. The white arrow indicates the site of stimulation.

FIG. 16: Comparison of temporal characteristics of HRF between HSPARSEfMRI and Nyquist acquisition fMRI following optogenetic stimulation ofthe dentate gyrus. Three subjects were optogenetically stimulated duringimaging, using the single (HSPARSE HSPARSE×1) and 3 times averaged(HSPARSE×3) high-resolution HSPARSE fMRI and a highest spatialresolution Nyquist acquisition (NAcq). For each subjects, thetime-to-peak difference between the HSPARSE and NAcq images was lessthan the 3 s temporal resolution. Although the duration of activity wassimilar between the HSPARSE and NAcq images for subject 1 and 3 onaverage, the duration was larger in the HSPARSE reconstructed image forsubject 2. This difference could be due biological variability since theNyquist acquisition datasets and the CS datasets were separatelyacquired in different fMRI imaging sessions.

FIG. 17: GPU based HSPARSE fMRI method achieved a 34-fold improvement inspeed. Three computationally expensive functions—the DCT, NUFFT andiNUFFT—were tested with the GPU method and its parallel CPU counterpart.The GPU methods showed 165-, 28-, and 108-fold improvements in speed,respectively, resulting in a 34-fold overall speedup.

The HSPARSE fMRI method was robust to motion within a normalphysiological range. (FIG. 18A) Five sets of motion profiles with amaximum absolute translation equivalent to 1- to 5-pixels were designed.To simulate realistic motion, the z-dimension translation was restrictedto be smaller than the x- and y-dimension translations, and rotationsabout the x-, y- and z-axis were limited to within ±0.5 degrees. Solidlines represent an example six degree-of-freedom motion profile andshaded areas represent the ranges of translations or rotations in eachmotion profile. (FIG. 18B) The motion corrected HSPARSE images showsimilar activations as the motion corrected original images when themotion was 1-5 pixels. (FIG. 18C, 18D) When the amount of motion wasless than 3-pixels, the HSPARSE reconstructed images were similar to theoriginal image as measured by the mean F-value, sensitivity and falsepositive rate. However, when motion was larger than 4-pixels, theHSPARSE reconstructed images exhibited decrease in mean F-value andsensitivity. (FIG. 18E) Motion profiles of the three experiment subjectsindicated that the physiological motion in the in vivo experiments werewell within the 4-pixel range of robust reconstruction with the HSPARSEmethod. They exhibited translations of less than 2.5-pixels androtations of less than ±0.2 degrees.

FIG. 19: Algorithm 1 was implemented on a Graphical Processing Unitplatform. Several repeatedly computations such as the non-uniform FFT(NUFFT), inverse NUFFT, DWT and inverse DWT were carefully optimized.For the NUFFT, a similar pre-sorting algorithm was implemented. A custombuild workstation was used for the real-time reconstruction with Intelquad-core 2.66 GHz CPU, Nvidia 2048 cores CUDA GPU and 16 GB CPU memory.

High reconstruction speed was achieved with FISTA method and GPUoptimization. (FIG. 20A) FISTA method showed a much faster convergencespeed and lower cost than the widely used conjugate gradient method.Because FISTA was not a descend method, an increase of the cost in someiterations was observed. (FIG. 20B) With GPU optimization, FISTA methodsuccessfully reconstructed a 140×140×32 matrix sized image within 605ms. Combining with the IGN motion correction and coherence analysis, theoverall real-time processing took less than 620 ms and only consists 20%of the consecutive image duration. (FIG. 20C) Repeatedly usedcomputations such as the NUFFT, iNUFFT, DWT, and iDWT were calculatedefficiently by the GPU.

FIG. 21: Optimized stack of VDS achieved high incoherent sampling andFISTA method successfully reconstructs the under-sampled image. Thenormalized image intensities are also shown across the yellow dashedline.

FIG. 22: Real-time high-resolution CS fMRI achieved improved CNR, meanF-value, sensitivity and low FPR. A range of parameters were tested toidentify the optimal regularization parameters for the real-timehigh-resolution CS fMRI. After comparing different metrics shown in theFigure, it was found that 1e⁻³ and 5e⁻⁴ offer the best trade-off betweenmetrics and result in improved CNR, mean F-value, sensitivity and lowFPR.

Real-time high-resolution CS fMRI resolves layer specific activity. Twodifferent types of HRFs with distinct peak HRF amplitude (FIG. 23A) andlatency (FIG. 23B) were added into a phantom with interleaved layerpattern. The real-time high-resolution CS fMRI method successfullyresolves the peak HRF amplitude and latency differences between the twolayers while the highest spatial resolution Nyquist acquisition failed.

Randomized variable density stack of spirals design. (FIG. 24A) Thecenter k-space is designed to have a higher density than the outerk-space. Incoherence is introduced by randomly disturb the angle of eachinterleaf and random skipping interleaves in the outer k-space. (FIG.24B) The effective field of view of the spiral trajectory is designed tofollow a series of exponential functions shown.

Before the present invention is further described, it is to beunderstood that this invention is not limited to particular embodimentsdescribed, as such may, of course, vary. It is also to be understoodthat the terminology used herein is for the purpose of describingparticular embodiments only, and is not intended to be limiting, sincethe scope of the present invention will be limited only by the appendedclaims.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit unlessthe context clearly dictates otherwise, between the upper and lowerlimit of that range and any other stated or intervening value in thatstated range, is encompassed within the invention. The upper and lowerlimits of these smaller ranges may independently be included in thesmaller ranges, and are also encompassed within the invention, subjectto any specifically excluded limit in the stated range. Where the statedrange includes one or both of the limits, ranges excluding either orboth of those included limits are also included in the invention.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although any methods andmaterials similar or equivalent to those described herein can also beused in the practice or testing of the present invention, the preferredmethods and materials are now described. All publications mentionedherein are incorporated herein by reference to disclose and describe themethods and/or materials in connection with which the publications arecited.

It must be noted that as used herein and in the appended claims, thesingular forms “a,” “an,” and “the” include plural referents unless thecontext clearly dictates otherwise. Thus, for example, reference to “anopsin” includes a plurality of such opsins and reference to “the carbonfiber” includes reference to one or more carbon fibers and equivalentsthereof known to those skilled in the art, and so forth. It is furthernoted that the claims may be drafted to exclude any optional element. Assuch, this statement is intended to serve as antecedent basis for use ofsuch exclusive terminology as “solely,” “only” and the like inconnection with the recitation of claim elements, or use of a “negative”limitation.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable sub-combination. All combinations of the embodimentspertaining to the invention are specifically embraced by the presentinvention and are disclosed herein just as if each and every combinationwas individually and explicitly disclosed. In addition, allsub-combinations of the various embodiments and elements thereof arealso specifically embraced by the present invention and are disclosedherein just as if each and every such sub-combination was individuallyand explicitly disclosed herein.

The publications discussed herein are provided solely for theirdisclosure prior to the filing date of the present application. Nothingherein is to be construed as an admission that the present invention isnot entitled to antedate such publication by virtue of prior invention.Further, the dates of publication provided may be different from theactual publication dates which may need to be independently confirmed.

DETAILED DESCRIPTION

The present disclosure provides methods and systems for high-resolutionfunctional magnetic resonance imaging (fMRI), including real-timehigh-resolution fMRI methods and systems.

In describing embodiments of the present disclosure, methods forhigh-resolution functional magnetic resonance imaging (fMRI) are firstdescribed, followed by a description of systems useful for performingthe subject methods.

Methods

Aspects of the present disclosure include a method for functionalmagnetic resonance imaging (fMRI) of a subject. In certain embodiments,the method is a compressed sensing (CS) high-resolution fMRI method.Compressed sensing refers to a signal processing method where an imagecan be reconstructed from a series of sampling measurements obtainedwith a sampling rate below the Nyquist sampling rate. In general, themethod may include obtaining one or more fMRI images of a target area ina subject. For instance, in general, the method may include applyingwith an MRI system (e.g., a permanent magnet or electromagnet of the MRIsystem) a magnetic field to a target area in a subject. In someinstances, the method also includes applying with the MRI system (e.g.,an RF coil of the MRI system) an excitation waveform (e.g., an RFexcitation waveform) to the target area in the subject to producedetectable image data (e.g., magnetic resonance (MR) signals) of thetarget area in the subject. One or more additional fields may also beapplied by the MRI system, such as, but not limited to, one or more shimfields using one or more shim coils, one or more gradient fields usingone or more gradient coils, and the like. In addition, the methodincludes acquiring the image data (e.g., with a receiver of the MRIsystem) and producing an image of the target area in the subject basedon the acquired image data.

The acquired image data may be saved in a computer-readable memory andanalyzed at a subsequent time (also referred to herein as “offline”processing or “offline” MRI). In other cases, the acquired image datamay be analyzed in real-time to produce the image of the target area inthe subject. By “real-time” is meant that the acquired signals areanalyzed by the MRI system (e.g., by a processor in the MRI system)immediately after signal acquisition and/or during signal acquisition.

In certain embodiments of offline fMRI, to produce the MR image data,the method may include applying an excitation waveform to the targetarea in the subject. In certain embodiments, the method includesapplying a pulse sequence to the target area in the subject. The pulsesequence may be a balanced steady state free precession (b-SSFP)sequence that is applied to the target area in the subject. In certaincases, the pulse sequence has an echo time (TE) of 50 ms or less, suchas 40 ms or less, or 30 ms or less, or 20 ms or less, or 10 ms or less,or 5 ms or less, or 3 ms or less, or 2 ms or less. In some instances,the pulse sequence has a TE of 2 ms. In certain cases, the pulsesequence has a repetition time (TR) of 500 ms or less, such as 400 ms orless, or 300 ms or less, or 200 ms or less, or 100 ms or less, or 50 msor less, or 25 ms or less, or 20 ms or less, or 10 ms or less, or 5 msor less. In some instances, the pulse sequence has a TR ranging from 5to 10 ms, such as from 7 to 10 ms, or from 8 to 10 ms, or from 9 to 10ms. In certain instances, the pulse sequence has a TR of 9.375 ms.

In some instances of offline MRI, the method includes acquiring imagedata (MR signals) of the target area in the subject. In certain cases,the method includes using a sampling trajectory. The sampling trajectorymay be a randomized sampling trajectory. For instance, the method mayinclude acquiring image data of the target area in the subject using arandomly undersampled trajectory, such as a randomly undersampledvariable density spiral (VDS) trajectory. In certain cases, the samplingtrajectory is a variable density spiral (VDS) trajectory, such as, forexample, a randomized under-sampling stack of multi-interleaf variabledensity spiral (VDS) trajectory. In certain instances, the total numberof interleaves at each kz-slice follows a Laplacian distribution. Forinstance, in some embodiments, the center k-space is more denselysampled than the outer k-space.

In certain embodiments of offline MRI, the sampling method has a fieldof view (FOV). For example, the sampling method may have a FOV of10×10×10 mm or more, such as 15×15×15 mm or more, or 20×20×15 mm ormore, or 25×25×15 mm or more, or 30×30×15 mm or more, or 35×35×15 mm ormore. In certain instances, the sampling method has a FOV of 35×35×16 mmIn some cases, the sampling method has a resolution of 1×1×1 mm or less,such as 0.75×0.75×0.75 mm or less, or 0.5×0.5×0.5 mm or less, or0.25×0.25×0.5 mm or less. In certain instances, the sampling method hasa resolution of 0.21×0.21×0.5 mm In certain embodiments, the samplingmethod achieves a sampling acceleration factor of 2 or more, such as 3or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 ormore, or 10 or more as compared to conventional fMRI. In some cases, thesampling method achieves a sampling acceleration factor of 2 or more. Insome cases, the sampling method achieves a sampling acceleration factorof 5 or more.

In certain embodiments of offline MRI, the method includes producing animage of the target area in the subject based on the acquired imagedata. For example, the method may include analyzing (also referred toherein as processing) the image data to produce the image of the targetarea. As such, in some instances, the method includes reconstructing animage from the acquired image data. In certain cases, the methodincludes reconstructing the image using a cost function, such as an L1regularized cost function. In certain instances, the method includesanalyzing/processing the image data using a spatial sparsifyingtransform, such as a discrete cosine transform (DCT). For instance, themethod may include regularizing the fMRI temporal domain using a DCT. Insome cases, the method includes regularizing the fMRI spatial domainusing a DCT. In some embodiments, the method includes regularizing boththe temporal domain and the spatial domain using a DCT.

In certain embodiments of offline fMRI, the method includesreconstructing the image using one or more regularization parameters.Regularization parameters of interest for offline fMRI processinginclude, but are not limited to, contrast to noise ratio (CNR), activevolume within the designed active region, mean F statistic value (meanF-value), normalized root mean squared error (NRMSE), and peakhemodynamic response function (HRF) amplitude. In certain instances, aset of regularization parameters is considered to be in an optimal rangeif the CNR, active volume within the designed mask, and mean F-value aregreater than that of the ground-truth, and its NRMSE is less than 105%of the minimum NRMSE found within the search range. For example, thesubject fMRI methods may produce images having a CNR of 1.5 or more,such as 2 or more, or 2.5 or more, or 3 or more, or 4 or more, or 5 ormore, or 6 or more, or 7 or more, or 8 or more, or 9 or more, or 10 ormore. In some cases, the subject fMRI methods may produce images havinga CNR of 1.5 or more. In some cases, the subject fMRI methods mayproduce images having a CNR of 2.5 or more.

In certain embodiments, the subject fMRI methods produce an image havinga spatial resolution of about 0.2×0.2×0.5 mm³ or greater. For example,the subject fMRI methods can produce images having a spatial resolutionof 1×1×1 mm³ or greater, such as 0.9×0.9×0.9 mm³ or greater, or0.8×0.8×0.8 mm³ or greater, or 0.7×0.7×0.7 mm³ or greater, or0.6×0.6×0.6 mm³ or greater, or 0.5×0.5×0.5 mm³ or greater, or0.4×0.4×0.5 mm³ or greater, or 0.3×0.3×0.5 mm³ or greater, or0.2×0.2×0.5 mm³ or greater, or 0.1×0.1×0.5 mm³ or greater. In certaininstances, the subject fMRI methods produce an image having a spatialresolution of 0.21×0.21×0.5 mm³. In certain instances, the subject fMRImethods produce an image having a spatial resolution ranging from0.1×0.1×0.5 mm³ to 1×1×1 mm³, such as from 0.1×0.1×0.5 mm³ to0.9×0.9×0.9 mm³, or from 0.1×0.1×0.5 mm³ to 0.8×0.8×0.8 mm³, or from0.1×0.1×0.5 mm³ to 0.7×0.7×0.7 mm³, or from 0.1×0.1×0.5 mm³ to0.6×0.6×0.6 mm³, or from 0.1×0.1×0.5 mm³ to 0.5×0.5×0.5 mm³, or from0.1×0.1×0.5 mm³ to 0.4×0.4×0.5 mm³, or from 0.1×0.1×0.5 mm³ to0.3×0.3×0.5 mm³. In certain embodiments, the subject fMRI methodsproduce an image having a spatial resolution ranging from 0.1×0.1×0.5mm³ to 0.3×0.3×0.5 mm³.

As described above, rather than offline processing of the MRI imagedata, the acquired image data can be processed in real-time.

In certain embodiments of real-time fMRI, to produce the MR image data,the method may include applying an excitation waveform to the targetarea in the subject. In certain embodiments, the method includesapplying a pulse sequence to the target area in the subject to produceimage data (MR signals) that can be acquired by the MRI system. As such,in some instances, the method includes acquiring the image data (MRsignals) of the target area in the subject. In certain cases, the methodincludes using a sampling trajectory. The sampling trajectory may be arandomized sampling trajectory. For instance, the method may includeacquiring image data of the target area in the subject using a randomlyundersampled trajectory, such as a randomly undersampled variabledensity spiral (VDS) trajectory. In certain cases, the samplingtrajectory is a variable density spiral (VDS) trajectory, such as, forexample, a randomized stack of variable density spiral (VDS) trajectory.In certain instances, the sampling density follows an exponentialfunction along the kx and ky plane, and the variance of the exponentialfunction decreases along the kz direction. In some instances, randomnessis introduced into the sampling for CS reconstruction by randomlyperturbing the angle of each spiral interleaf. In certain cases, thetrajectory has a slightly larger total number of interleaves, andinterleaves on the outer k-space are randomly skipped following aGaussian distribution to achieve the desired temporal resolution. Insome instances, the kz-slice location may be adjusted to achievevariable density sampling in the kz dimension and high spatialresolution in the z dimension.

In certain embodiments of real-time MRI, the sampling method has a fieldof view (FOV). For example, the sampling method may have a FOV of10×10×10 mm or more, such as 15×15×15 mm or more, or 20×20×15 mm ormore, or 25×25×15 mm or more, or 30×30×15 mm or more, or 35×35×15 mm ormore. In certain instances, the sampling method has a FOV of 35×35×16 mmIn some cases, the sampling method has a resolution of 1×1×1 mm or less,such as 0.75×0.75×0.75 mm or less, or 0.5×0.5×0.5 mm or less, or0.25×0.25×0.5 mm or less. In certain instances, the sampling method hasa resolution of 0.25×0.25×0.5 mm In certain embodiments, the samplingmethod achieves a sampling acceleration factor of 2 or more, such as 3or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 ormore, or 10 or more as compared to conventional fMRI. In some cases, thesampling method achieves a sampling acceleration factor of 2 or more. Insome cases, the sampling method achieves a sampling acceleration factorof 5 or more.

In certain embodiments of real-time MRI, the method includes producingan image of the target area in the subject based on the acquired imagedata. For example, the method may include analyzing (also referred toherein as processing) the image data to produce the image of the targetarea. As described herein, the image data may be processed in real-timeto produce the image of the target area. As such, in some instances, themethod includes reconstructing an image from the acquired image data inreal-time. In certain cases, the method includes reconstructing theimage using a cost function, such as an L1 spatial regularized costfunction. In certain instances, the method includes analyzing/processingthe image data using a sparsifying transform, such as a Daubechies 4wavelet. In some cases, the method includes analyzing/processing theimage data using a fast iterative shrinkage thresholding algorithm(FISTA).

In certain embodiments of real-time fMRI, the method includesreconstructing the image using one or more regularization parameters.Regularization parameters of interest for real-time fMRI processinginclude, but are not limited to, contrast to noise ratio (CNR), mean Fstatistic value (mean F-value), normalized root mean squared error(NRMSE), peak HRF amplitude, sensitivity, and false positive rate in thereconstructed dataset. In certain instances, a set of regularizationparameters is considered to be in an optimal range if the parametersgive top 50% CNR, mean F-value, sensitivity, and bottom 50% NRMSE andfalse positive rate. For example, the subject fMRI methods may produceimages having a CNR of 1.5 or more, such as 2 or more, or 2.5 or more,or 3 or more, or 4 or more, or 5 or more, or 6 or more, or 7 or more, or8 or more, or 9 or more, or 10 or more. In some cases, the subject fMRImethods may produce images having a CNR of 1.5 or more. In some cases,the subject fMRI methods may produce images having a CNR of 2.5 or more.

In certain embodiments of the present disclosure, the method is a methodfor functional MRI (fMRI). For example, in general, the presentdisclosure provides a method for monitoring activity in an organ ortissue of an individual (also referred to as “a subject” herein). Insome instances, the target organ or tissue is an excitable organ ortissue in the subject. “Excitable,” as used herein, refers toelectrically excitable cells in an organ or tissue, such as neurons andmuscle cells. Excitable cells typically use changes in their membranepotential to transmit signals within the cell. Thus, an excitable cellmay be characterized in having a resting state, where the membranepotential is at the resting membrane potential, and an excited state,where rapid depolarization of the membrane potential is transmittedacross the cell as an action potential. The “cellular electricalactivity” of an excitable cell may refer to the changes in the membranepotential or may refer to any indirect measure of the changes inmembrane potential, such as the changes in intracellular calciumconcentration or any other biochemical changes that is a functionalmeasure of the change in the membrane potential.

In certain embodiments, the method includes surgically implanting adevice of the present disclosure into or adjacent to an organ or tissueof an individual, and monitoring the activity of the organ or tissueusing fMRI. In some cases, surgically implanting the device includesopening an access in the subject and inserting at least a portion of thedevice through the access. The access may be an access through the skin,bone, muscle, and/or other tissues of the subject. For instance, anaccess may include an access through bone (e.g., skull) of the subjectto allow placement of at least a portion of the device (e.g., anoptrode) adjacent to target neurons in the subject.

As indicated above, embodiments of the method include monitoring theactivity of the organ or tissue. In some instances, monitoring theactivity of the organ or tissue includes conducting functional magneticresonance imaging (fMRI) on the organ or tissue. In some cases, theorgan or tissue includes excitable cells (e.g., cells that express oneor more light-responsive polypeptides). The terms “light-activated” and“light-responsive” in reference to a polypeptide or protein that islight-responsive, are used interchangeably and include light-responsiveion channels or opsins, and ion pumps as described herein. Suchlight-responsive proteins may have a depolarizing or hyperpolarizingeffect on the cell on whose plasma membrane the protein is expresseddepending on the ion permeability of the activated protein, and theelectrochemical gradients present across the plasma membrane.

In some cases, the one or more light-responsive polypeptides include ahyperpolarizing light-responsive polypeptide. In some cases, the one ormore light-responsive polypeptides include a depolarizinglight-responsive polypeptide. As such, in some cases the method includesproducing an image of the target organ or tissue using fMRI. In somecases, fMRI may be used to image the organ or tissue prior to deliveringlight to the target organ or tissue using the optrode. In some cases,fMRI may be used to image the organ or tissue during delivery of lightto the target organ or tissue using the optrode. In some cases, fMRI maybe used to image the organ or tissue after delivering light to thetarget organ or tissue using the optrode.

The method may further include detecting and/or recording a detectableparameter of the organ or tissue using the device (e.g., optrode). Theoptrode may be configured to detect electrical signals, such as localfield potentials produced by changes in the membrane potential of theexcitable cells. Thus, in some cases, the method includes detectingand/or recording a detectable parameter of the organ or tissue using acarbon fiber electrode of the optrode.

The device (e.g., optrode) may include a light source. In theseembodiments, the method includes delivering light to the target organ ortissue using the light source. For instance, the method may includestimulating the excitable cells in the target organ or tissue with lightfrom the light source. In some cases, the light source includes anoptical fiber as described herein. As such, in these embodiments, themethod includes delivering light to the target organ or tissue using theoptical fiber (e.g., stimulating the excitable cells with lightdelivered by the optical fiber). In some cases, the light sourceincludes a laser. As such, in some embodiments, the method includesdelivering light to the target organ or tissue using the laser. Forexample, the method may include generating light using the laser anddirecting the light from the laser to the target organ or tissue usingthe optical fiber (e.g., for stimulating the excitable cells in thetarget organ or tissue with light from the laser). In some cases, thelight source includes a light-emitting diode (LED). As such, in someembodiments, the method includes delivering light to the target organ ortissue using the LED. For instance, the method may include generatinglight using the LED and directing the light from the LED to the targetorgan or tissue using the optical fiber (e.g., for stimulating theexcitable cells in the target organ or tissue with light from the LED).

In certain embodiments, the detectable parameter of the target organ ortissue includes local field potentials, e.g., local field potentialsproduced by changes in the membrane potential of the excitable cells.The local field potentials may be produced by stimulating the excitablecells with light from the light source. In some instances, thedetectable parameter is a single-unit activity, e.g., detectableactivity from a single target area (i.e., a uniplex assay). In somecases, the detectable parameter is a multi-unit activity, e.g.,detectable activity from two or more target areas (i.e., a multiplexassay).

In some instances, monitoring the activity of the organ or tissue isperformed once. In other cases, monitoring the activity of the organ ortissue is performed two or more times. In some cases, monitoring theactivity of the organ or tissue is performed several times over a periodof time, e.g., the method includes chronically monitoring the activityof the organ or tissue. In some cases, monitoring the activity of theorgan or tissue may be performed over an extended period of time, suchas 1 day or more, 2 days or more, 3 days or more, 4 days or more, 5 daysor more, 6 days or more, 7 days or more, 8 days or more, 9 days or more,10 days or more, such as, for example, 1 week or more, 2 weeks or more,3 weeks or more, 1 month or more, 2 months or more, 3 months or more, 4months or more, 5 months or more, 6 months or more, 7 months or more, 8months or more, 9 months or more, 10 months or more, 11 months or more,1 year or more, or ever longer periods of time.

In some cases, the individual is a human. In some cases, the individualis a non-human primate. In some cases, the individual is a rodent (e.g.,a rat, a mouse, etc.). The tissue or organ (e.g., “target tissue” or“target organ”) may be an in vivo neuronal tissue, a tissue slicepreparation, a nerve fiber bundle, a neuromuscular junction, etc. The invivo neuronal tissue may be neuronal tissue of an animal that isanesthetized or non-anesthetized, and is restrained or non-restrained.The target tissue of interest includes, but is not limited to, theneocortex, the hypothalamus, entorhinal and hippocampal formationcortex, mammillary bodies, septum, bed nucleus of stria terminalis,dorsal and ventral striatum, thalamus, amygdala, accumbens, brainstem,subcortical structures in general, muscle, spinal cord, cardiac tissue,etc.

In some embodiments, the excitable cells (e.g., neurons) in a targettissue or organ are genetically modified to express a light-responsivepolypeptide that, when stimulated by an appropriate light stimulus,hyperpolarizes or depolarizes the stimulated excitable cell. The term“genetic modification” refers to a permanent or transient genetic changeinduced in a cell following introduction into the cell of a heterologousnucleic acid (i.e., nucleic acid exogenous to the cell). Genetic change(“modification”) can be accomplished by incorporation of theheterologous nucleic acid into the genome of the host cell, or bytransient or stable maintenance of the heterologous nucleic acid as anextrachromosomal element. Where the cell is a eukaryotic cell, apermanent genetic change can be achieved by introduction of the nucleicacid into the genome of the cell. Suitable methods of geneticmodification include viral infection, transfection, conjugation,protoplast fusion, electroporation, particle gun technology, calciumphosphate precipitation, direct microinjection, and the like.

In some instances, the light-responsive polypeptide is a light-activatedion channel polypeptide.

The light-activated ion channel polypeptides are adapted to allow one ormore ions to pass through the plasma membrane of a target cell when thepolypeptide is illuminated with light of an activating wavelength.Light-activated proteins may be characterized as ion pump proteins,which facilitate the passage of a small number of ions through theplasma membrane per photon of light, or as ion channel proteins, whichallow a stream of ions to freely flow through the plasma membrane whenthe channel is open. In some embodiments, the light-responsivepolypeptide depolarizes the excitable cell when activated by light of anactivating wavelength. In some embodiments, the light-responsivepolypeptide hyperpolarizes the excitable cell when activated by light ofan activating wavelength.

In some embodiments, the light-responsive polypeptides are activated byblue light. In some embodiments, the light-responsive polypeptides areactivated by green light. In some embodiments, the light-responsivepolypeptides are activated by yellow light. In some embodiments, thelight-responsive polypeptides are activated by orange light. In someembodiments, the light-responsive polypeptides are activated by redlight.

In some embodiments, the light-responsive polypeptide expressed in acell can be fused to one or more amino acid sequence motifs selectedfrom the group consisting of a signal peptide, an endoplasmic reticulum(ER) export signal, a membrane trafficking signal, and/or an N-terminalgolgi export signal. The one or more amino acid sequence motifs whichenhance light-responsive protein transport to the plasma membranes ofmammalian cells can be fused to the N-terminus, the C-terminus, or toboth the N- and C-terminal ends of the light-responsive polypeptide. Insome cases, the one or more amino acid sequence motifs which enhancelight-responsive polypeptide transport to the plasma membranes ofmammalian cells is fused internally within a light-responsivepolypeptide. Optionally, the light-responsive polypeptide and the one ormore amino acid sequence motifs may be separated by a linker. In someembodiments, the light-responsive polypeptide can be modified by theaddition of a trafficking signal (ts) which enhances transport of theprotein to the cell plasma membrane. In some embodiments, thetrafficking signal can be derived from the amino acid sequence of thehuman inward rectifier potassium channel Kir2.1. In some embodiments,the signal peptide sequence in the protein can be deleted or substitutedwith a signal peptide sequence from a different protein.

Exemplary light-responsive polypeptides and amino acid sequence motifsthat find use in the present system and method are disclosed in, e.g.,PCT App. Nos. PCT/US2011/028893 and PCT/US2015/23087.

The individual may be any suitable individual for analyzing theindividual's brain functional activity data. In some cases, theindividual is a human individual. In some cases the human is a healthyhuman, or a human having a neurological disorder. The neurologicaldisorder may be any suitable neurological disorder. In some cases, theneurological disorder is caused by a disease, e.g., a neurologicaldisease. The neurological disease may be any suitable disease associatedwith pathological activity of a network of neurons. Suitableneurological diseases include, without limitation, Parkinson's disease,Alzheimer's disease, dementia, epilepsy, autism, bipolar disorder,schizophrenia, Tourette's syndrome, obsessive compulsive disorder,attention deficit hyperactivity disorder, Huntington's disease, multiplesclerosis, or migraine. In some embodiments, the neurological disorderis an age-related disorder of brain function.

In certain embodiments, the methods may be used to treat a disease orcondition (e.g., a neurological disorder) in the subject that isamenable to treatment using the subject methods. As used herein, theterms “treat,” “treatment,” “treating,” and the like, refer to obtaininga desired pharmacologic and/or physiologic effect. The effect may beprophylactic in terms of completely or partially preventing a disease orsymptom thereof and/or may be therapeutic in terms of a partial orcomplete cure for a disease and/or adverse effect attributable to thedisease. “Treatment,” as used herein, covers any treatment of a diseasein a mammal, particularly in a human, and includes: (a) preventing thedisease from occurring in a subject which may be predisposed to thedisease but has not yet been diagnosed as having it; (b) inhibiting thedisease, i.e., arresting its development; and (c) relieving the disease,e.g., causing regression of the disease, e.g., to completely orpartially remove symptoms of the disease.

Selective activation of neurons in order to measure subtype-specificfunctional activity may be done using any suitable method. Suitablemethods of selective neuron activation include, without limitation,optogenetic stimulation, single unit electrophysiology, etc. Where theneurons are selectively activated by optogenetic stimulation, theneurons may express one or more light-activated polypeptides configuredto hyperpolarize or depolarize the neurons. Suitable light-activatedpolypeptides and methods used thereof are described further below.

Light-Activated Polypeptides

A light-activated polypeptide of the present disclosure may be anysuitable light-activated polypeptide for selectively activating neuronsof a subtype by illuminating the neurons with an activating lightstimulus. In some instances, the light-activated polypeptide is alight-activated ion channel polypeptide. The light-activated ion channelpolypeptides are adapted to allow one or more ions to pass through theplasma membrane of a target cell when the polypeptide is illuminatedwith light of an activating wavelength. Light-activated proteins may becharacterized as ion pump proteins, which facilitate the passage of asmall number of ions through the plasma membrane per photon of light, oras ion channel proteins, which allow a stream of ions to freely flowthrough the plasma membrane when the channel is open. In someembodiments, the light-activated polypeptide depolarizes the cell whenactivated by light of an activating wavelength. In some embodiments, thelight-activated polypeptide hyperpolarizes the cell when activated bylight of an activating wavelength. Suitable hyperpolarizing anddepolarizing polypeptides are known in the art and include, e.g., achannelrhodopsin (e.g., ChR2), variants of ChR2 (e.g., C128S, D156A,C128S+D156A, E123A, E123T), iC1C2, C1C2, GtACR2, NpHR, eNpHR3.0, C1V1,VChR1, VChR2, SwiChR, Arch, ArchT, KR2, ReaChR, ChiEF, Chronos, ChRGR,CsChrimson, and the like. In some cases, the light-activated polypeptideincludes bReaCh-ES, as described herein and described further in, e.g.,Rajasethupathy et al., Nature. 2015 Oct. 29; 526(7575):653, which isincorporated by reference. Hyperpolarizing and depolarizing opsins havebeen described in various publications; see, e.g., Berndt and Deisseroth(2015) Science 349:590; Berndt et al. (2014) Science 344:420; and Guruet al. (Jul. 25, 2015) Intl. J. Neuropsychopharmacol. pp. 1-8 (PMID26209858).

The light-activated polypeptide may be introduced into the neurons usingany suitable method. In some cases, the neurons of a subtype of interestare genetically modified to express a light-activated polypeptide. Insome cases, the neurons may be genetically modified using a viralvector, e.g., an adeno-associated viral vector, containing a nucleicacid having a nucleotide sequence that encodes the light-activatedpolypeptide. The viral vector may include any suitable control elements(e.g., promoters, enhancers, recombination sites, etc.) to controlexpression of the light-activated polypeptide according to neuronalsubtype, timing, presence of an inducer, etc.

Neuron-specific promoters and other control elements (e.g., enhancers)are known in the art. Suitable neuron-specific control sequencesinclude, but are not limited to, a neuron-specific enolase (NSE)promoter (see, e.g., EMBL HSENO2, X51956; see also, e.g., U.S. Pat. Nos.6,649,811, 5,387,742); an aromatic amino acid decarboxylase (AADC)promoter; a neurofilament promoter (see, e.g., GenBank HUMNFL, L04147);a synapsin promoter (see, e.g., GenBank HUMSYNIB, M55301); a thy-1promoter (see, e.g., Chen et al. (1987) Cell 51:7-19; and Llewellyn etal. (2010) Nat. Med. 16:1161); a serotonin receptor promoter (see, e.g.,GenBank S62283); a tyrosine hydroxylase promoter (TH) (see, e.g., Nucl.Acids. Res. 15:2363-2384 (1987) and Neuron 6:583-594 (1991)); a GnRHpromoter (see, e.g., Radovick et al., Proc. Natl. Acad. Sci. USA88:3402-3406 (1991)); an L7 promoter (see, e.g., Oberdick et al.,Science 248:223-226 (1990)); a DNMT promoter (see, e.g., Bartge et al.,Proc. Natl. Acad. Sci. USA 85:3648-3652 (1988)); an enkephalin promoter(see, e.g., Comb et al., EMBO J. 17:3793-3805 (1988)); a myelin basicprotein (MBP) promoter; a CMV enhancer/platelet-derived growth factor-βpromoter (see, e.g., Liu et al. (2620) Gene Therapy 11:52-60); a motorneuron-specific gene Hb9 promoter (see, e.g., U.S. Pat. No. 7,632,679;and Lee et al. (2620) Development 131:3295-3306); and an alpha subunitof Ca(²⁺)-calmodulin-dependent protein kinase II (CaMKIIα) promoter(see, e.g., Mayford et al. (1996) Proc. Natl. Acad. Sci. USA 93:13250).Other suitable promoters include elongation factor (EF) 1α and dopaminetransporter (DAT) promoters.

In some cases, neuronal subtype-specific expression of thelight-activated polypeptide may be achieved by using recombinationsystems, e.g., Cre-Lox recombination, Flp-FRT recombination, etc. Celltype-specific expression of genes using recombination has been describedin, e.g., Fenno et al., Nat Methods, 2014 Jul.; 11(7):763; and Gompf etal., Front Behav Neurosci. 2015 Jul. 2; 9:152, which are incorporated byreference herein.

Systems

Aspects of the present disclosure include a functional magneticresonance imaging (fMRI) system. In certain embodiments, the system isconfigured for compressed sensing (CS) high-resolution fMRI. In general,the fMRI system is configured to obtain one or more fMRI images of atarget area in a subject. For instance, in general, the MRI systemincludes a permanent magnet or electromagnet of the MRI system thatapplies a magnetic field to a target area in a subject. In someinstances, the system also includes an RF coil that applies anexcitation waveform (e.g., an RF excitation waveform) to the target areain the subject to produce detectable image data (e.g., magneticresonance (MR) signals) of the target area in the subject. One or moreadditional coils may also be included in the MRI system, such as, butnot limited to, one or more shim coils that apply one or more shimfields, one or more gradient coils that apply one or more gradientfields, and the like. In addition, the system includes a receiver (e.g.,a receiver coil) that acquires image data (MR signals). The system mayalso include a processor configured to producing an image of the targetarea in the subject based on the acquired image data.

As described herein, the fMRI system may be configured for offlineprocessing of the image data, where the acquired image data is saved ina computer-readable memory and analyzed at a subsequent time. In othercases, the fMRI system is configured for real-time processing of theacquired image data, where the acquired image data is analyzed inreal-time to produce the image of the target area in the subject.

In certain embodiments, the fMRI system may include an MRI device, aprocessor, and a memory (e.g., a non-transient memory on acomputer-readable medium). For example, the memory may contain anapplication or program that, when executed by the processor, causes theMRI device to record functional activity of an individual's brain togenerate functional activity data for the individual, and furtherperform a method of analyzing functional activity data, as describedherein.

The MRI device may be any suitable MRI device, such as an MRI deviceconfigured to perform the high-resolution fMRI methods described herein.Suitable MRI devices are described in, e.g., U.S. Pat. No. 8,834,546,which is incorporated herein by reference.

In certain embodiments, the subject fMRI devices (and systems) areconfigured to produce an image having a spatial resolution of about0.2×0.2×0.5 mm³ or greater. For example, the subject fMRI devices (andsystems) can be configured to produce images having a spatial resolutionof 1×1×1 mm³ or greater, such as 0.9×0.9×0.9 mm³ or greater, or0.8×0.8×0.8 mm³ or greater, or 0.7×0.7×0.7 mm³ or greater, or0.6×0.6×0.6 mm³ or greater, or 0.5×0.5×0.5 mm³ or greater, or0.4×0.4×0.5 mm³ or greater, or 0.3×0.3×0.5 mm³ or greater, or0.2×0.2×0.5 mm³ or greater, or 0.1×0.1×0.5 mm³ or greater. In certaininstances, the subject fMRI devices (and system) are configured toproduce an image having a spatial resolution of 0.21×0.21×0.5 mm³. Incertain instances, the subject fMRI devices (and system) are configuredto produce an image having a spatial resolution ranging from 0.1×0.1×0.5mm³ to 1×1×1 mm³, such as from 0.1×0.1×0.5 mm³ to 0.9×0.9×0.9 mm³, orfrom 0.1×0.1×0.5 mm³ to 0.8×0.8×0.8 mm³, or from 0.1×0.1×0.5 mm³ to0.7×0.7×0.7 mm³, or from 0.1×0.1×0.5 mm³ to 0.6×0.6×0.6 mm³, or from0.1×0.1×0.5 mm³ to 0.5×0.5×0.5 mm³, or from 0.1×0.1×0.5 mm³ to0.4×0.4×0.5 mm³, or from 0.1×0.1×0.5 mm³ to 0.3×0.3×0.5 mm³. In certainembodiments, the subject fMRI devices (and system) are configured toproduce an image having a spatial resolution ranging from 0.1×0.1×0.5mm³ to 0.3×0.3×0.5 mm³. In some cases, a processor of the device (orsystem) is configured to produce an image having a spatial resolution asdescribed herein.

In certain embodiments, the system includes one or more processing units(also called herein “processors”), memory (i.e., a computer readablestorage medium), an input/output (I/O) interface, and a communicationsinterface. These components communicate with one another over one ormore communication buses or signal lines. In some embodiments, thememory, or the computer readable storage media of memory, stores anoperating system, programs, modules, instructions, and stored data. Theone or more processors are coupled to the memory and operable to executethese programs, modules, and instructions, and read/write from/to thestored data. In certain embodiments, the programs include one or more ofthe algorithms as described herein that are used to apply waveforms tothe target area in the subject, acquire MR signals, and/or analyze theacquired image data.

In some embodiments, the processing units include one or moremicroprocessors, such as a single core or multi-core microprocessor. Insome embodiments, the processing units include one or more generalpurpose processors. In some embodiments, the processing units includeone or more special purpose processors specifically programmed to applywaveforms to the target area in the subject, acquire MR signals, and/oranalyze the acquired image data using one or more of the algorithms, asdescribed herein.

In some cases, the processor is configured to analyze the signals inreal-time. In other cases, the acquired signals are saved by theprocessor in a memory for subsequent analysis of the data (also referredto herein as offline processing).

In some embodiments, the memory includes high-speed random accessmemory, such as DRAM, SRAM, DDR RAM or other random access solid statememory devices. In some embodiments the memory includes non-volatilememory, such as one or more magnetic disk storage devices, optical diskstorage devices, flash memory devices, or other non-volatile solid statestorage devices. In some embodiments, the memory includes one or morestorage devices remotely located from the processing units. The memory,or alternately the non-volatile memory device(s) within the memory,includes a computer readable storage medium. In some embodiments, thememory includes a non-transitory computer readable storage medium.

In some embodiments, the I/O interface is coupled to one or moreinput/output devices, such as one or more displays, keyboards,touch-sensitive surfaces (such as a track pad or a touch-sensitivesurface of the touch-sensitive display), speakers, and microphones. TheI/O interface may be configured to receive user inputs (e.g., voiceinput, keyboard inputs, etc.) from a user and process them accordingly.The I/O interface may also be configured to present outputs (e.g.,sounds, images, text, etc.) to the user according to various programinstructions implemented on the system.

In some embodiments, the communications interface includes wiredcommunication port(s) and/or wireless transmission and receptioncircuitry. The wired communication port(s) receive and sendcommunication signals via one or more wired interfaces, e.g., Ethernet,Universal Serial Bus (USB), FIREWIRE, etc. The wireless circuitryreceives and sends RF signals and/or optical signals from/tocommunications networks and other communications devices. The wirelesscommunications may use any of a plurality of communications standards,protocols and technologies, such as GSM, EDGE, CDMA, TDMA, Bluetooth,Wi-Fi, VoIP, Wi-MAX, or any other suitable communication protocol. Thenetwork communications interface enables communication between thesystem with networks, such as the Internet, an intranet and/or awireless network, such as a cellular telephone network, a wireless localarea network (LAN) and/or a metropolitan area network (MAN), and otherdevices. Network communications interface is configured to facilitatecommunications between the system and other devices over a network.

In some aspects, the system may include a computer, which may be apersonal device (e.g., laptop, desktop, workplace computer, portabledevice, etc.). A computer that is a personal device may not need to beconnected to a network. In some aspects, the computer is a server or acollection of servers, and may not need an I/O interface. For example,the computer may be a server, and a neural pathway analysis program ofthe present disclosure may be accessed by a user through a website.

In some embodiments, the operating system (e.g., LINUX®, UNIX®, OS X®,WINDOWS®, or an embedded operating system) includes various softwarecomponents and/or drivers for controlling and managing general systemtasks (e.g., memory management, storage device control, powermanagement, etc.) and facilitates communications between varioushardware, firmware, and software components.

It should be noted that the system is only one example, and that thesystem may have more or fewer components than shown, may combine two ormore components, or may have a different configuration or arrangement ofthe components. The various components of the system may be implementedin hardware, software, firmware, including one or more signal processingand/or application specific integrated circuits, or a combination ofthereof.

A neural pathway analysis program that includes one or more programs maybe stored in the memory, and include instructions to perform methodsaccording to one or more embodiments of the above methods section. Theneural pathway analysis program may include any of the followingexemplary modules or a subset or a superset thereof.

In some cases, a neural pathway analysis program may be configured tocomputationally process functional activity data for a region of a brainof an individual, as described above, to generate an estimate of therelative activities of neural pathways regulated by each of a pluralityof neuronal subtypes, by generating a connectivity model from thefunctional activity data based on a network model of functionalconnections among interconnected nodes representing the region, asdescribed above; and deriving a set of coefficients from a linearregression between a) the connectivity model; and b) neuronalsubtype-specific connectivity estimates among the interconnected nodes,as described above.

The present system may include an fMRI device, configured to measurefunctional brain activity of an individual. The computer system may bein communication with the fMRI device, through the communicationinterface, such that the computer system can control operation of thefMRI device and/or retrieve functional imaging data from the fMRIdevice.

The neural pathway analysis program may include a model-generatingmodule, e.g., a spDCM module, configured to generate the connectivitymodel from the functional activity data based on a network model offunctional connections among interconnected nodes representing theregion.

The neural pathway analysis program may include a linear regressionmodule configured to perform a linear regression between a) theconnectivity model; and b) neuronal subtype-specific connectivityestimates, to derive a set of coefficients that represent thecontribution to the functional activity data of a neural pathwayregulated by different neuronal subtypes.

The methods described herein may be performed by the computer system. Insome embodiments, the computer system is a distributed computer system.For example, the computer system includes a first set of one or moreprocessors located remotely from a second set of one or more processors.In some embodiments, the computer system includes a web serverconfigured to provide a web interface. In some embodiments, the webinterface is configured to receive data. In some embodiments, the webinterface is configured to display results.

In certain aspects, the neural pathway analysis program, may beconfigurable by a user. For example, a the neural pathway analysisprogram may include a user interface module (not shown) configured toenable a user to determine one or more settings, such as the networkmodel, neuronal subtype-specific connectivity estimates, whether toinclude neural fluctuations, etc., to apply to the model generatingand/or linear regression algorithms, or any other settings that wouldallow for one or more embodiments described in the above methodssection.

In some embodiments, the system includes a brain stimulation device,such as a deep brain stimulation device or a transcranial magneticstimulation device, configured to stimulate a brain region of theindividual being monitored by the fMRI device. In some instances, thebrain stimulation device is an optrode. In some embodiments, thecomputer system may be configured to control the brain stimulationdevice based on the analysis of neural pathways contributing to thefunctional brain activity data, according to methods of the presentdisclosure. For example, if the neural pathway analysis indicates thatan individual has insufficient activity in a neural pathway associatedwith a neurological disorder from which the individual suffers, thecomputer system may provide an appropriate stimulation to the relevantbrain region that regulates the neural pathway via the brain stimulationdevice, thereby rebalancing the level of the neural pathway activity inthe individual's brain.

Embodiments of the present disclosure may include an implantable device,such as an optrode. Certain embodiments of the subject optrodes can beused to detect electrical signals (and/or changes in electricalsignals), such as electrical signals produced near the optrode duringuse. In some cases, the optrode is configured to detect an electricalsignal, such as a local field potential (LFP). An LFP is anelectrophysiological signal (electrical potential, or voltage) generatedby the summed electric current flowing from multiple nearby neuronswithin a localized volume of nervous tissue. Voltage is produced acrossthe local extracellular space by action potentials and graded potentialsin neurons in the area, and can vary as a result of synaptic activity.For instance, the subject optrode can detect cellular electricalactivity of an excitable cell, such as neurons and muscle cells.

In some cases, the optrode is adapted for use in magnetic resonanceimaging, such as functional MRI (fMRI). In certain embodiments, theoptrode is configured for uniplex analysis of a target area (e.g.,target tissue or organ) in a subject. By “uniplex analysis” is meantthat a single target area is analyzed using the devices and methodsdisclosed herein. For example, a single optrode may be used for analysisof one target area in a subject. In these embodiments, the optrode isconfigured for detection and analysis of single-unit activity in asubject.

Other embodiments include the multiplex analysis of two or more targetareas (e.g., target tissues or organs) in a subject. By “multiplexanalysis” is meant that the two or more areas of excitable cells may beanalyzed using the devices and methods disclosed herein. For example,the system may include two or more optrodes. In some instances, thenumber of target areas for analysis using multiplex devices as disclosedherein is 2 or more, such as 4 or more, 6 or more, 8 or more, 10 ormore, etc., up to 20 or more, e.g., 50 or more, including 100 or more,or 500 or more distinct target areas. In certain embodiments, thedevices and methods may be used for the multiplex analysis of 2 to 500distinct target areas in the subject, such as 2 to 250 distinct targetareas, including 2 to 100 distinct target areas, or 2 to 50 distincttarget areas, or 2 to 25 distinct target areas, or 2 to 10 distincttarget areas. In certain embodiments, 2 or more multiplex assays may beconducted in parallel substantially simultaneously.

As discussed above, the system of the present disclosure may beconfigured for multiplex analysis, such that the optrode is configuredfor detection and analysis of multi-unit activity in a subject. As such,the optrode may be configured to include an array of electrodes. An“array” includes any arrangement of individually addressable electrodes.An array is “addressable” when it has multiple electrodes and eachelectrode may carry a signal independent of the other electrodes in thearray. Thus, an array of electrodes may be used to detect distinctsignals from different target tissues or organs in a subject. An arraymay contain 2 or more, 4 or more, 8 or more, 10 or more, 50 or more, 100or more, 250 or more, or 500 or more electrodes.

Embodiments of the device may also include a light source. In somecases, the light source includes an optical fiber. The optical fiber maybe configured to direct light to a target area (e.g., a target tissue ororgan) in a subject. For example, the optical fiber may direct light toa target area in the subject that contains excitable cells, such asneurons or muscle cells. As discussed herein, the excitable cells (e.g.,neurons) in a target tissue or organ may be genetically modified toexpress a light-responsive polypeptide that, when stimulated by anappropriate light stimulus, hyperpolarizes or depolarizes the stimulatedexcitable cell. Thus, the optical fiber may be used to direct light tothe target tissue or organ to stimulate the excitable cells. Asdiscussed herein, the optrode may be used to detect electrical signalsand/or changes in electrical signals produced by the excitable cells. Insome cases, the distal end of the optical fiber is positioned adjacentto the target area in the subject. Light emitted from the distal end ofthe optical fiber may stimulate the excitable cells as discussed herein.In certain instances, the proximal end of the optical fiber is attachedto a source of light. The source of light may be any source of lightsuitable for performing a desired assay, such as, for example, a sourceof light that produces light of an appropriate wavelength to stimulatethe excitable cells in the target area of the subject. In some cases,the light source is a laser. In some cases, the light source is a lightemitting diode (LED). In some cases, two or more light sources may beincluded in the device, such as light sources that produce light ofdifferent wavelengths. In some cases, the device also includes anoptical switch.

Utility

Embodiments of the methods and systems described herein find use in avariety of MRI applications, such as MRI methods and systems wherehigh-resolution MRI images are desired. In some cases, the subjectmethods and systems find use in producing high-resolution functional MRI(fMRI) images of a target area in an individual. For instance, thesubject methods and systems find use in fMRI techniques for measuringthe brain activity of an individual, such as by detecting changesassociated with blood flow in one or more target areas in the brain ofthe individual. In other cases, the subject methods and systems find usein producing high-resolution functional MRI (fMRI) images of a targetarea in an individual, where the activity in excitable cells in a targetorgan or tissue in the individual is assessed. As described herein, thesubject methods and systems may find use in detecting the activity oflight-responsive polypeptides (e.g., light-activated ion channels) inexcitable cells (e.g., neurons) in the individual. As such, the subjectmethods and systems find use in global and/or regional brain functionstudies, such as where the activity of one or more target regions of thebrain is mapped in high-resolution.

In certain embodiments, the subject methods and systems find use inproducing high-resolution fMRI images of a target area in an individual,including high-resolution fMRI images that are produced offline (i.e.,where processing of the image data is performed at a time after theimage data is acquired), and also high-resolution fMRI images that areproduced in real-time (i.e., where processing of the image data occursimmediately following acquisition of the image data and/or duringacquisition of the image data).

In some embodiments, the present methods and systems find use inscreening in vitro and/or in vivo animal models of disease for neuronalcircuit elements diagnostic of or causative for neuropsychiatricdisease. For example, the present methods and systems find use inpre-surgical brain function diagnosis. Embodiments of the presentmethods and systems also find use in planning brain machine interface,such as by mapping the neuronal activity in areas of the brain todetermine appropriate locations in the brain for brain machineinterface.

In some embodiments, the present methods and systems find use indiagnosis of neuropsychiatric diseases of interest, which may includedisorders of mood and affect, anxiety, psychosis, personality, etc. Insome instances, an animal model may be used. The animal model may be anysuitable model, including, but not limited to, rodents, cats, dogs,monkeys, and non-human primates. Perturbations used to model aneuropsychiatric disease include genetic models of neurological orpsychiatric disease, such as autism; chronically induced models as withkainate or pilocarpine-induced epilepsy or chronic stress-induceddepression; and acutely induced models as with hallucinogens orpsychotogenic agents such as ketamine or phencyclidine (PCP). Bycomparing the difference in activity pattern between neurons in normaltarget tissue and neurons in abnormal target tissue, neural correlatesof the neuropsychiatric disorder may be identified. Optical control ofneurons in the target tissue may then allow identification of causativeneuronal activity patterns for a particular neuropsychiatric disorder.These manipulations may potentially provide novel treatment targets. Assuch, in some embodiments, the present methods and systems find use indiagnostic methods for neuropsychiatric diseases, e.g., where thediagnosis is carried out on a human or non-human mammalian subject.

In some embodiments, the present methods and systems find use in methodsfor identifying a treatment, e.g., a therapeutic treatment, with adesired activity on a group of neurons. If the desired outcome is known,then the present methods and systems may be used to screen fortreatments, including, but not limited to, pharmacological agents,nonchemical based therapeutic treatment; behavioral treatment;electrical, magnetic, or optical based neural-modulation treatment;etc., that will bring about the desired neuronal activity pattern. Thescreening may be performed in any suitable animal model, either normal,or a model for a neurological disorder, such as Alzheimer's andParkinson's disease, mild cognitive impairment, other dementias, andDown's Syndrome, as well as schizophrenia, autism, mood, affective,anxiety, and personality/developmental disorders, or other diseasemodels described herein.

In some embodiments, the present methods and systems find use in thetreatment of a condition or disorder, such as a neurological orpsychiatric condition using optogenetic control. As real-time activityof neurons is monitored using the present methods and systems, acontroller or processor may be configured to modulate the activity ofneurons in response to the imaged activity signals in such a way as totreat or reduce symptoms of the condition or disorder, at the behavioraland/or physiological levels.

Computer Related Embodiments

A variety of computer-related embodiments are also provided.Specifically, the data analysis methods described herein may beperformed using a computer, e.g., a processor. Accordingly, provided isa computer-based system for analyzing data produced using the abovemethods and systems in order to provide qualitative and/or quantitativeanalysis of a target area of interest in a subject.

In certain embodiments, the methods are coded onto a computer-readablemedium in the form of “programming”, where the term “computer readablemedium” as used herein refers to any storage or transmission medium thatparticipates in providing instructions and/or data to a computer forexecution and/or processing. Examples of storage media include CD-ROM,DVD-ROM, BD-ROM, a hard disk drive, a ROM or integrated circuit, amagneto-optical disk, a solid-state memory device, a computer readableflash memory, and the like, whether or not such devices are internal orexternal to the computer. A file containing information may be “stored”on computer readable medium, where “storing” means recording informationsuch that it is accessible and retrievable at a later date by a computer(e.g., for offline processing). Examples of media include, but are notlimited to, non-transitory media, e.g., physical media in which theprogramming is associated with, such as recorded onto, a physicalstructure. Non-transitory media for storing computer programming doesnot include electronic signals in transit via a wireless protocol.

In certain embodiments, computer programming may include instructionsfor directing a computer to perform one or more steps of the methodsdisclosed herein. For example, the computer programming may includeinstructions for directing a computer to detect and/or analyze signalsacquired by the systems and devices disclosed herein (e.g., thepresently disclosed fMRI systems). In certain embodiments, the computerprogramming includes instructions for directing a computer to analyzethe acquired MR signals qualitatively and/or quantitatively. Qualitativedetermination includes determinations in which a simple yes/no result isprovided to a user with respect to the presence or absence of adetectable signal. Quantitative determination includes bothsemi-quantitative determinations in which a rough scale result, e.g.,low, medium, high, is provided to a user regarding the detectable signaland fine scale results in which an exact measurement of the detectablesignal is provided to a user (e.g., a quantitative measurement of localfield potentials in a target area of interest).

In some embodiments, the computer programming includes instructions fordirecting a computer to perform a uniplex analysis of an analyte in asample. By “uniplex analysis” is meant that detection and analysis isperformed on a single target area in the subject. For example, a singletissue area in the subject containing excitable cells may be analyzed.In some embodiments, the computer programming includes instructions fordirecting a computer to perform a multiplex analysis of two or moretarget areas in a subject. By “multiplex analysis” is meant that the twoor more distinct areas of interest in a subject are analyzed. Forexample, two or more distinct tissue areas in the subject eachcontaining excitable cells may be analyzed. In certain embodiments, thecomputer programming includes instructions for directing a computer toperform several multiplex assays in parallel substantiallysimultaneously.

With respect to computer readable media, “permanent memory” refers tomemory that is not erased by termination of the electrical supply to acomputer or processor. Computer hard-drive, CD-ROM, DVD-ROM, BD-ROM, andsolid state memory are all examples of permanent memory. Random AccessMemory (RAM) is an example of non-permanent memory. A file in permanentmemory may be editable and re-writable. Similarly, a file innon-permanent memory may be editable and re-writable.

EXAMPLES

The following examples are put forth so as to provide those of ordinaryskill in the art with a complete disclosure and description of how tomake and use the present invention, and are not intended to limit thescope of what the inventors regard as their invention nor are theyintended to represent that the experiments below are all or the onlyexperiments performed. Efforts have been made to ensure accuracy withrespect to numbers used (e.g. amounts, temperature, etc.) but someexperimental errors and deviations should be accounted for. Unlessindicated otherwise, parts are parts by weight, molecular weight isweight average molecular weight, temperature is in degrees Celsius, andpressure is at or near atmospheric. Standard abbreviations may be used,e.g., bp, base pair(s); kb, kilobase(s); pl, picoliter(s); s or sec,second(s); min, minute(s); h or hr, hour(s); aa, amino acid(s); kb,kilobase(s); bp, base pair(s); nt, nucleotide(s); i.m.,intramuscular(ly); i.p., intraperitoneal(ly); s.c., subcutaneous(ly);and the like.

Two versions of compressed sensing (CS) high-resolution functionalmagnetic resonance imaging (fMRI) were developed for offline andreal-time experiments. There were three different parts in each version,including data acquisition paradigm, reconstruction algorithm, andregularization parameter optimization strategy.

In the offline CS high-resolution fMRI, a randomized under-samplingstack of multi-interleaf variable density spiral (VDS) trajectory wasused. The total number of interleaves at each kz-slice followed aLaplacian distribution, where the center k-space was more denselysampled than the outer k-space. The sampling paradigm achieved 35×35×16mm FOV and 0.21×0.21×0.5 mm resolution, which was about 6 times higherthan the standard fMRI resolution. This sampling paradigm wasimplemented into a passband balanced steady state free precession(b-SSFP) sequence with TE=2 ms, and TR=9.375 ms. To enable full braincoverage without b-SSFP banding, two acquisitions were performed and thetwo phase-cycled images were combined by maximum intensity projection.The under-sampled image in the offline CS high-resolution fMRI wasiteratively reconstructed using an L1 regularized cost function, whereboth the fMRI temporal and spatial domain were regularized by discretecosine transform (DCT). The cost function was solved by gradient descentmethod. Many computationally intensive calculations such as thenon-uniform fast Fourier transform (NUFFT), matrix arithmetic and DCTwere implemented on the graphics processing unit, which achieved 34-foldoverall improvement in reconstruction speed compared to the CPUimplementation. The optimal regularization parameter for the offline CShigh-resolution fMRI reconstruction was identified by reconstructingphantoms with a range of regularization parameters and monitoring fivedifferent metrics of the reconstructed images. Three distinct phantomsat a normal (30 dB) and low (25 dB) signal to noise ratio with differentbackground images were used. Six-cycle 20s-on-40s-off canonical SPMhemodynamic response function (HRF) were added to phantoms. The fivemonitored metrics included the contrast to noise ratio, active volumewithin the designed active region, mean F statistic value, normalizedroot mean squared error (NRMSE) and peak HRF amplitude. A set ofregularization parameters was considered to be in the optimal range ifits corresponding CNR, active volume within the designed mask, and meanF-value were greater than that of the ground-truth, and its NRMSE wasless than 105% of the minimum NRMSE found within the search range.

In the real-time CS high-resolution fMRI, a randomized stack of variabledensity spiral sampling paradigm was used. The sampling density followedan exponential function along the kx and ky plane and the variance ofthe exponential function decreased along the kz direction. Randomnesswas introduced into the sampling for CS reconstruction by randomlyperturbing the angle of each spiral interleaf. The trajectory also had aslightly larger total number of interleaves and then interleaves on theouter k-space were randomly skipped following a Gaussian distribution toachieve the desired temporal resolution. This trajectory was implementedwith 35×35×16 mm FOV and 0.25×0.25×0.5 mm spatial resolution, which was4 times higher than the resolution of the standard fMRI. Theundersampled dataset was reconstructed in real-time using an L1 spatialregularized cost function. Daubechies 4 wavelet was used for thesparsifying transform. The fast iterative shrinkage thresholdingalgorithm (FISTA) was applied to solve the cost function, whichconverged in approximately 30 iterations. The optimal regularizationparameters were identified for the real-time high-resolution fMRI byreconstructing phantoms with a range of regularization parameters andmonitoring the CNR, mean F-value, NRMSE, peak HRF amplitude, sensitivityand false positive rate in the reconstructed dataset. The optimalregularization parameters were identified as the intersect of theparameters that gave top 50% CNR, mean F-value, sensitivity, and bottom50% NRMSE and false positive rate.

Example 1

In preclinical studies, a High SPAtial Resolution compressed SEnsing(HSPARSE) fMRI method was tested and a systematic evaluation wasperformed to assess CS high spatial resolution fMRI. fMRI spatialresolution for the HSPARSE method was maximized from multiple aspects:optimization of the contrast with a balanced steady state freeprecession (b-SSFP) sequence, which enabled T2 microvascular sensitiveimaging (Kim et al., 2012, Park et al., 2011) with low spatialdistortion (Lee et al., 2010, Scheffler et al., 2003, Miller et al.,2006, Lee et al., 2003).

In order to validate this approach, data acquisition was optimized witha randomly under-sampled variable density spiral (VDS), which enabled6-fold spatial resolution improvement with an acceleration factor of5.3, which was designed with a balanced steady state free precessionsequence to achieve high spatial resolution data acquisition. This ratiowas 32% greater than that reported in previous CS fMRI studies with asingle coil (Holland et al., 2013, Zong et al., 2014). A modified k-tSPARSE method was then implemented and applied with a strategy tooptimize regularization parameters for consistent, high quality CSreconstruction.

The performance and reliability of the HSPARSE method was thenevaluated. Because the CS reconstruction was substantially dependent onthe regularization parameters, the roles of the spatial and temporalregularizations were investigated. Accordingly, systematic evaluation ofthe capability of the HSPARSE in detecting functional differencesbetween neighboring active regions (e g laminar specificity) wasperformed. Lastly, the HSPARSE method was tested in vivo withoptogenetic fMRI (ofMRI) experiments.

The results indicated that the methods of the present disclosureimproved spatial resolution by 6-fold with 12 to 47% contrast-to-noiseratio (CNR), 33 to 117% F-value improvement and maintained the sametemporal resolution. It also achieved high sensitivity of 69 to 99%compared the original ground-truth, small false positive rate of lessthan 0.05 and low HRF distortion across a wide range of CNRs. The methodwas robust to physiological noise and enabled detection oflayer-specific activities in vivo, which cannot be resolved using thehighest spatial resolution Nyquist acquisition.

The methods of the present disclosure enabled high spatial resolutionfMRI that could resolve layer-specific brain activity and demonstratedthe significant improvement that CS can bring to high spatial resolutionfMRI.

Materials and Methods

A randomly under-sampled variable density spiral trajectory enabling anacceleration factor of 5.3 was designed with a balanced steady statefree precession sequence to achieve high spatial resolution dataacquisition. A modified k-t SPARSE method was then implemented andapplied with a strategy to optimize regularization parameters forconsistent, high quality CS reconstruction. In this section, the methodsand design of HSPARSE were described, and subsequently the in vivotesting of the HSPARSE method with optogenetic fMRI (ofMRI) experimentsis described.

1.1 Randomly Under-Sampled, Variable-Density Spiral, b-SSFP DataAcquisition

A randomized under-sampling VDS b-SSFP sequence was designed for theHSPARSE fMRI method. Compared to conventional gradient-recalled-echo(GRE) sequence, the b-SSFP sequence provided lower spatial distortionand better activity localization with T2 microvascular sensitivity. TheVDS trajectory also provided high sampling efficiency and robustnessagainst motion, off-resonance, and gradient artifacts. In addition,using spiral trajectory for CS also resulted in the highest CNR comparedto most CS under-sampling trajectories available (Jeromin et al., 2012).

A high spatial resolution Nyquist acquisition (FIG. 1B) required longeracquisition time and resulted in lower temporal resolution compared tothe low spatial resolution Nyquist acquisition (FIG. 1A) with the samefield of view (FOV). To enable high spatial resolution withoutsacrificing the temporal resolution, the sampling was accelerated by1.77 times with a 3D VDS trajectory consisting of 32 kz locations and 30interleaves at each kz location (FIG. 1C, 1E). Then, 320 interleaveswere randomly selected from the 3D VDS trajectory (FIG. 1D, 1F), whichfurther accelerated the sampling by another factor of 3 and resulted inan overall acceleration factor of 5.3. To obtain more interleaves at thecenter k-space, the number of interleaves at each kz slice was designedto follow a Laplacian distribution (mean μ=16, scaling b=11). Theunder-sampling patterns were also randomized across temporal frames toexploit the fMRI temporal sparsity. However, the total number ofinterleaves was identical across temporal frames to achieve constanttemporal resolution. This design not only enabled rapid data acquisitionbut also successfully achieved the incoherent sampling for effective CSreconstruction.

The HSPARSE passband b-SSFP acquisition scheme was implemented on a 7Tesla Bruker scanner with a single transmit and receive surface coilusing TE=2 ms, TR=9.375 ms, and flip angle=30°. With a 35×35×16 mm FOV,the sequence was designed to obtain spatial resolution of 0.21×0.21×0.5mm and 167×167×32 matrix size at a 3 s temporal resolution. Afully-sampled b-SSFP sequence was also designed with the highest spatialresolution achievable (0.5×0.5×0.5 mm resolution, 70×70×32 matrix size)while maintaining the same FOV and temporal resolution. To enable fullbrain coverage without b-SSFP banding at either resolution, twoacquisitions were performed with 0° and 180° phase-cycling angles. Thetwo phase-cycled images were then combined by maximum intensityprojection (Lee et al., 2008).

1.2 Accelerated CS fMRI Reconstruction

Similar to CS MRI, spatial sparsity of each fMRI frame was used toreconstruct an under-sampled fMRI dataset (Holland et al., 2013, Huggeret al., 2011, Asslander et al., 2013):

$\begin{matrix}{{\underset{m}{argmin}\mspace{14mu} {f(m)}} = {{\frac{1}{2}{{{Fm} - y}}_{2}^{2}} + {\lambda {{\Psi_{s}m}}_{1}}}} & \lbrack 1\rbrack\end{matrix}$

where F represents the non-uniform Fast Fourier transform (NUFFT)(Fessler et al., 2007) in the spatial domain, m was the 4D image underreconstruction, y was the randomly under-sampled 4D kt-space data, λ wasthe regularization coefficient, and Ψ_(s) represents a spatialsparsifying transform such as the discrete cosine transform (DCT) ordiscrete wavelet transform (DWT). As shown by Holland et al. 2013, theperformance of this algorithm was largely dependent on the trajectorydesign and the algorithm may fail at a large under-sampling ratio.Therefore, an alternative constraint utilizing temporal redundancy(Gamper et al., 2008, Otazo et al., 2010, Zong et al., 2014) has beendeveloped:

$\begin{matrix}{{\underset{m}{argmin}\mspace{14mu} {f(m)}} = {{\frac{1}{2}{{{Fm} - y}}_{2}^{2}} + {\lambda {{\Psi_{t}m}}_{1}}}} & \lbrack 2\rbrack\end{matrix}$

where Ψ_(t) was the sparsifying transform along the temporal dimension.Low complexity transforms such as the Fourier transform and DCT can beapplied in block-design fMRI, and more complicated transforms such asthe Karhunen Loeve Transform (KLT) and DWT was recommended in complexdesigns such as event-related fMRI.

In the current implementation, a modified kt-SPARSE (Lustig et al.,2006) technique was utilized where both the temporal and spatialredundancies were exploited:

$\begin{matrix}{{\underset{m}{argmin}\mspace{14mu} {f(m)}} = {{\frac{1}{2}{{{Fm} - y}}_{2}^{2}} + {\lambda_{1}{{\Psi_{t}m}}_{1}} + {\lambda_{2}{{\Psi_{s}m}}_{1}}}} & \lbrack 3\rbrack\end{matrix}$

This cost function allows an acceleration factor and reconstructionquality no worse than methods utilizing only spatial or temporalsparsity constraints because its solution set contains optimal solutionsof both the spatial and temporal regularization methods. Compared to thecost function that utilizes the 4D sparsifying transform, this methodalso gives higher image contrast, lower noise level, and lower falsepositive rate (FIG. 2).

DCT was chosen for both the spatial and temporal sparsifying transformin the implementation. It has previously been shown that the FT canserve as an effective transform for block-designed CS fMRI. Because DCThas a higher compression ratio than the FT, therefore, DCT can enablebetter reconstruction for the block-designed fMRI. In addition, DCT alsohas lower complexity than the data-driven KLT and faster speed than themulti-level-decomposition based DWT on a parallel platform. In order tosystematically optimize the reconstruction and investigate hundreds ofreconstructions under a wide range of regularization parameters, DCT waschosen to facilitate this process.

A fast implementation of the gradient descent method was applied (Boydet al., 2004) (FIG. 3 and FIG. 4) to solve equation [3]. All keyprocessing on a GPU was computed, which reduced the reconstruction timefrom 18 hours to 30 minutes and achieved 34-fold acceleration.Implementation details can be found in the supporting information.

1.3 Optimization of Regularization Parameters

High-speed reconstruction achieved by the GPU parallelization enabledexhaustive testing of the HSPARSE reconstruction across differentregularization parameters and phantoms to investigate roles of thespatiotemporal regularizations and to identify optimal parameters forvarious imaging conditions. Three distinct phantoms (denoted A1, B1 andC1) at a normal (30 dB) and low (25 dB) SNR (FIG. 5A) were designed. TheA1 phantom was designed to simulate an in vivo experiment, while B1 andC1 were designed to test across different base images and activationpatterns. The base image of the B1 phantom was generated from a rat MRItemplate (Schwarz et al., 2006). Six-cycle 20s-on-40s-off canonical SPMhemodynamic response function (HRF) were added to phantoms in this andfollowing simulations. The peak HRF amplitudes were set to be 10%modulation of the baseline at the activation pattern center, anddecreased with distance following a Gaussian function. After a coarseparameter search to find potential optimal parameter range, thefollowing range of parameters were investigated: λ1 from 1e−2 to 5e−5with 8 steps and λ2 from 5e−3 to 1e−9 with 14 steps. Because values ofthe λ1 and λ2 were dependent on the value of the sampled k-space, thek-space of each dataset was normalized by its corresponding maximumabsolute values (.∥k_(sig)∥_(∞)=1.) to enable comparison of the λ1 andλ2 among different reconstructions.

The data was analyzed with five-gamma basis set general linear model(GLM) using the SPM. F-test was then conducted and active voxels wererecognized as those having an F-value greater than 4.42 (P<0.001). A setof regularization parameters was considered to be in the optimal rangeif its corresponding CNR, active volume within the designed activeregion, and mean F-value were greater than that of the ground-truth, andits normalized root mean squared error (NRMSE) between the ground-truthand the HSPARSE reconstructed images was less than 105% of the minimumNRMSE found within the search range. The peak HRF amplitude wascalculated as the maximum value of the GLM regressed HRF. And the CNRwas estimated as the peak HRF amplitude divided by the standarddeviation of the residual time-series.

1.4 Evaluation of fMRI Signal Sensitivity and False Positive Rate

To quantitatively evaluate the sensitivity and false positive rate (FPR)of the HSPARSE fMRI method in 3D space, three additional 30 dB phantomswere designed (A2, B2, and C2) in which activity was confined to asingle imaging slice with sharp contrast boundaries (FIG. 5B). Thissingle-slice pattern was designed to evaluate the sensitivity and FPRunder the most difficult conditions, and higher sensitivity and lowerFPR was expected in real application. Four different peak HRF amplitudes(10, 8, 6 and 4%, corresponding to CNR 2.55, 2.05, 1.58 and 1.23) weretested. As shown in FIG. 6A, the sensitivity was quantified as the ratiobetween the number of true positive voxels and the number of truepositive plus false negative voxels. The FPR was evaluated as the ratiobetween the number of false positive voxels and the number of falsepositive plus true negative voxels in the first to fifth pixel perimeterlayer in 3D (FIG. 6B).

1.5 Evaluation of HRF Temporal Characteristics

Because temporal redundancy was utilized to reconstruct images in CSfMRI, reconstruction may introduce a certain amount of temporaldistortion into the resulting fMRI signal. Therefore, the temporalcharacteristics of the HSPARSE fMRI signal were evaluated to determinethe nature of the resulting distortion. Three phantoms (A3, B3, and C3)were designed with SNR of 30 dB and peak HRF amplitudes of 10, 8, 6 and4% (FIG. 5C). Each phantom was reconstructed 5 times with independentidentical Gaussian distributed noise using previously identified optimalparameters (λ₁=5e-3 and λ₂=1e-4).

1.6 Evaluation of Robustness to Physiological Noise

Physiological noise in fMRI can lead to decreased sensitivity, increasedFPR, and temporal distortion (Kruger et al., 2001), which could be moresevere in CS fMRI due to the under-sampling. Therefore, the robustnessof the HSPARSE fMRI was evaluated with fully-sampled in vivo datasetscontaining physiological noise. However, because a fully-sampled highspatial resolution fMRI dataset cannot be acquired without reducing thetemporal resolution (due to the fundamental hardware limitations), thehighest spatial resolution was acquired in fully-sampled datasetsinstead. Three fully-sampled in vivo dentate gryus stimulation datasetswere acquired. This data was then under-sampled by 5.3 times andreconstructed with the HSPARSE method for comparison.

1.7 Systematic Evaluation of Spatial Resolution

After separately evaluating spatial and temporal distortions, thecapability of the HSPARSE method was systematically evaluated to detectfunctional differences between neighboring active regions, such as thelaminar specific responses. Three 35 dB phantoms were designed withincreasing difficulty in resolving activations. First, three regionswith clear boundaries consisting of signals with distinct peak HRFamplitudes (15, 10 and 5%) (FIG. 7A) or latencies (FIG. 7D) werecreated. Second, patterns with interleaved layers consisting of signalswith different peak HRF amplitudes (12 and 4%, FIG. 7A) or latencies(FIG. 8D) were designed. Spatial distortion in the first one may onlyresult in ambiguous region boundary whereas spatial distortion in thesecond phantom can result in complete loss of layers. Third, the spatialresolution limit of the HSPARSE fMRI was further explored using anactivity pattern containing different widths of interleaved signalsgoing down to the single pixel (FIG. 8G, 8H). These activity patternswere added to six continuous slices for each phantom.

1.8 In Vivo Evaluation with Optogenetic Dentate Gyrus Stimulation

Finally, the spatial resolution, sensitivity, and temporal distortion ofthe HSPARSE method was compared to the highest spatial resolutionNyquist acquisition in vivo by selectively stimulating dentate gyrusexcitatory neurons in three rats using optogenetics. Optogenetic fMRIwas performed because it allows precise stimulation of target brainregions, which can result in highly localized activations for evaluatingimprovements in spatial resolution. The dentate gyrus was targeted (FIG.9A) due to its unique layered shape (FIG. 9B). It also forms the inputstructure of the hippocampus and its unique unidirectional natureprovides a predictable pattern of activity in hippocampus and downstreamstructures, which can help evaluate the spatial resolution of differentfMRI methods. The data was acquired at 3 s temporal resolution and eachdataset included 10 baseline frames and 120 frames of 6-cycle20s-on-40s-off optical stimulation. After reconstruction, subject motionwas corrected by a GPU based motion correction method (Fang et al.,2013).

The peak HRF amplitude was calculated for voxels that has an F-valuelarger than 4.42 (P<0.001) and overlaid the peak HRF amplitude map ontoa high-resolution atlas MRI image (86) to enable precise anatomicallocalization of the activity. Since an equivalent fully-sampled highspatial resolution fMRI dataset could not be acquired due to fundamentallimitations, the HSPARSE fMRI method was compared with the achievablehighest spatial resolution Nyquist image with the same FOV and temporalresolution (NAcq). The SNR of the Nyquist and the HSPARSE images wereapproximately 30 dB and 25 dB, respectively. Therefore, three HSPARSEdatasets were averaged to obtain a high-resolution image with the sameSNR as the Nyquist image

(25+20 log₁₀√{square root over (3)}≈30).

Results

2.1 Optimal Regularization Parameters were Identified

The role of the regularization parameters was first assessed on the 30dB A1 phantom reconstruction. As shown in FIG. 10A, the reconstructedimage quality and fMRI signal depends significantly on the choice of thetemporal and spatial regularization parameters. As shown in FIG. 10B,although the reconstructed peak HRF amplitude generally decreased forall tested regularization parameters, it was found that parameter rangesresulted in higher CNR, mean F-value, and larger active volume withinthe original designed region than the ground-truth phantom. When theseranges were overlaid, a parameter range was identified for λ1 between1e⁻² and 5e⁻³ and λ2 between 1e⁻⁴ and 1e⁻⁹ (blue area in FIG. 10B lowerright—white arrow) that maximizes the CNR, mean F-value, and the activevolume within the original designed active region with low NRMSE.Therefore, it was concluded that this range of parameters was optimalfor this phantom.

The same tests were performed at higher noise levels (25 dB) and withdifferent base images (B1 phantom) and activation patterns (C1 phantom)(FIG. 5A). A common range was identified (dark blue range in FIG.10C—black arrow) where all the optimal ranges for different phantomsoverlapped. For example, for λ1=5e⁻³ and λ2=1e⁻⁴, which was within thisrange, CNR improves by 12 to 47%, mean F-value by 33 to 117%, activevolume by 36 to 178%, and the NRMSE was less than 0.24. (FIG. 10D-H).The point spread function FWHM also decreased from 0.70 mm for thehighest spatial resolution Nyquist image to 0.32 mm (FIG. 10I) for theHSPARSE image with the optimal regularization parameters. Although theaverage peak HRF amplitude was reduced, it was later shown that the HRFshape and relative amplitude was maintained after reconstruction.

2.2 High Sensitivity and Low False Positive Rate

The sensitivity (FIG. 6A) and FPR (FIG. 6B) was evaluated using the 30dB A2, B2 and C2 phantoms (FIG. 5B) at four different peak HRFamplitudes (10-4%, corresponding to CNR of 2.55-1.23). Examplereconstructions of the three phantoms with 10% peak HRF amplitude wereshown in FIG. 6C. It was found that the activities were localized to theoriginal volume of activation and the false positive signals werelimited. As shown in FIG. 6D, the sensitivities of the HSPARSEreconstructed datasets were found to be 69 to 99% of the originaldatasets on average. It was also found that the FPRs were small on allperimeter layers and across all tested peak HRF amplitudes, e.g. theFPRs on the 1-pixel perimeter layer (FPR1) were less than 0.051 and theFPRs on the 2-pixel perimeter layer (FPR2) were less than 0.01. Becausethe single-slice activity patterns were designed for testing under themost difficult conditions, higher sensitivity and lower FPR was expectedin real applications. Taken together, these data indicated that theHSPARSE reconstruction yielded high sensitivity than the ground-truthimage with a low FPR.

2.3 Low HFR Distortion

The temporal distortion of the HSPARSE fMRI method was investigated witha range of HRFs (peak amplitudes of 10-4%) typically observed in in vivoexperiments (FIG. 11A). As shown in FIG. 11A, 11B, although the HRFamplitude was reduced, the HRF shape was maintained with the HSPARSEreconstruction. The HSPARSE reconstructed HRF amplitudes exhibitedstrong linear correlation with the original HRF amplitudes (R2=0.98,FIG. 11C), indicating that the HSPARSE reconstruction linearly scaledthe original HRF with little distortion to the HRF shape. HRFnormalization also confirmed that the temporal dynamics were wellpreserved following the HSPARSE reconstruction, i.e., mean differencesbetween normalized HRFs were less than 0.016 and limits of agreement(1.96×standard deviation) were less than 0.08 (FIG. 11D) across alltested peak HRF amplitudes.

HRF characteristics such as the duration of activation and time-to-peakwere also compared. The activity duration of the A3 phantom was notsignificantly different between the original and the HSPARSEreconstructed images for any of the four tested HRF amplitudes (FIG.11E, Wilcoxon ranksum test). For time-to-peak, the maximum meandifference between CS and the original signal of the A3 phantom was 2.4s (FIG. 11F). Because this time-to-peak difference was less than 3 s,which was the temporal resolution of the data acquisition, thedifferences could simply be a result of having a limited temporalresolution. Similar results were also observed for the B3 and C3phantoms.

2.4 Robust Against Physiological Noise

Here, the fully-sampled datasets were compared with real physiologicalnoise to their corresponding 5.3 times under-sampled HSPARSE images. Asshown in FIG. 12A, consistent increases in CNR, mean F-value, and activevolume were observed following the HSPARSE reconstructions. The NRMSEsbetween the HSPARSE reconstructed and the fully-sampled in vivo datasetswere lower than 0.081 across subjects. The HSPARSE reconstructeddatasets detect most of the activity in the fully-sampled datasets (FIG.12B). The common active voxels in both images were 90% to 93% of theactive voxels in the fully-sampled images (FIG. 12C). Additional activevoxels were also observed in the HSPARSE reconstructed datasets. Becausethe ground-truth active region was not available for the in vivodatasets, these additional active voxels could either be false positivedetections or a result of the improved CNR. Importantly, these signalswere within the 1-voxel perimeter layer of the original active regionacross all subjects, indicating the HSPARSE activity localization wasconsistent with the fully-sampled image. The HSPARSE reconstructed HRFswas linear to the fully-sampled HRFs and the correlation coefficientswere larger than 0.98 (FIG. 12D). Measures of the activation durationand time-to-peak also confirmed that the HRFs from the fully-sampledimages and the HSPARSE reconstruction were close (FIG. 13).

2.5 Resolves High Spatial Resolution Functional Contrast

The HSPARSE method was systematically evaluated to determine whether itcan detect differences between functionally distinct yet spatiallyadjacent regions. The first test was performed on a three-layer phantomwith distinct peak HRF amplitudes of 15, 10 and 5% (FIG. 7A). With thehighest spatial resolution Nyquist acquisition, the borders between thethree layers became obscured (FIG. 7B). However, using the HSPARSEmethod, the three layers were well preserved with clear boundaries amonglayers. The HSPARSE raw HRFs and their corresponding first two principalcomponent analysis (PCA) coefficients also showed better separation thanthose of the Nyquist image (FIG. 7C). A similar test was conducted usingthe same phantom containing three HRFs with distinct latency (FIG. 7D).Similarly, the borders among three layers became obscured with theNyquist acquisition (FIG. 7E), while the three layers were accuratelyresolved by the HSPARSE method (FIG. 7F).

The second test was performed on a phantom with a pattern containing 6interleaved layers of high and low (12 and 4%) amplitude activity (FIG.8A). With the Nyquist acquisition, the layer specificity cannot beresolved (FIG. 8B). However, the HSPARSE fMRI accurately reconstructedthe 6 layers of activation with clear boundaries (FIG. 8C). Similarresults were also achieved with two HRFs with distinct latency (FIG. 8D,8E).

Finally, the resolution limit of the HSPARSE fMRI method wasinvestigated using an interleaved pattern phantom with minimum layerthickness ranging from 1- to 4-pixels. As shown in FIG. 8G, withdifferent peak amplitudes across layers, the HSPARSE method successfullyresolved layers down to 1-pixel (0.21 mm) thickness while the Nyquistacquisition can only distinguish the differences down to 3-pixel (0.63mm). Similarly, the HSPARSE method also showed high spatial resolutiondown to 1-pixel thickness with layers containing distinct latency butthe Nyquist acquisition failed when layer thickness below 3 pixels (FIG.8H).

With this test, some sinusoidal variations in the HSPARSE reconstructedHRFs were observed (FIG. 7C, 8C and FIG. 14C, 14D Third plot from thetop). This was likely due to the DCT regularization and can be reducedby utilizing higher compression ratio transforms such as KLT and DWT.However, importantly, key HRF characteristics were found and theirdifferences among layers were well preserved.

2.6 Resolves In Vivo Layer-Specific Activation Localized to the DentateGyrus

The optimized HSPARSE fMRI method was further tested in vivo usingoptogenetic stimulation of the rat dentate gyrus. The dentate gyrus wastargeted (FIG. 9A) due to its well-defined layered structure (FIG. 9B)and its function as a gate of the unidirectional hippocampal circuit,which can provide predictable activity patterns in hippocampus anddownstream structures. Histological examination confirmed thatChannelrhodopsin2-EYFP expression was localized to the dentate gyrus(FIG. 9C), indicating successful targeting of this region for lightstimulation. For fMRI, both the Nyquist and HSPARSE reconstructed imagesrevealed activity in the dentate gyrus. However, in both the single andthe three times averaged HSPARSE images, high peak HRF amplitude (e.g.top 40%) activities were found to be localized precisely following thegeometry of the molecular layer in the dentate gyrus while there was noobvious pattern observed in the Nyquist image (FIG. 9D). The Nyquistimage also showed activity in adjacent CA1. As reported by Angenstein etal., dentate gyrus activity may be localized or, when activitypropagates beyond the dentate gyrus, the entire hippocampal circuit wasactivated together with downstream regions such as the ipsilateralentorhinal cortex and subiculum. Since no activity was detected in thesedownstream regions, it was likely that the adjacent CA1 activitydetected by the Nyquist scan reflected a partial volume effect due tolow spatial resolution. Similar results were observed when theexperiments were repeated in two other subjects, indicating theconsistency of the HSPARSE method's precise spatial localization ability(FIG. 15).

Finally, it was found that HRFs of the single and the three timesaveraged HSPARSE images and the Nyquist images have strong linearcorrelation for all three subjects (FIG. 9E, 9F). This indicates thatthe temporal characteristics were consistent between the Nyquist and theHSPARSE images. Also, all time-to-peak differences between the HSPARSEand the Nyquist images were found to be within the 3 s temporalresolution (FIG. 16). While the durations of activity in two subjectswere similar for the HSPARSE and the Nyquist methods on average, theduration was different for one of the subjects. This may be due tobiological variation in this subject because the Nyquist and the HSPARSEimages were acquired in different imaging sessions. In conclusion, theseresults demonstrated that HSPARSE can improve localization and identifylayer-specific activities in vivo.

2.7 HSPARSE fMRI

HSPARSE fMRI can provide approximately 6-fold improvement in spatialresolution and resolve layer-specific activations in vivo. The HSPARSEfMRI was shown to support an acceleration factor of 5.3, which was 32%higher than the previous fMRI studies using a single coil setup (Hollandet al., 2013, Zong et al., 2014). With the HSPARSE fMRI, improved CNR,high sensitivity, low FPR, and low HRF distortion was achieved withoutcompromising temporal resolution. The HSPARSE fMRI also showed highrobustness against physiological noise, and was capable of resolvinglayer specific activations. Finally, in vivo experiments revealed thatthe HSPARSE fMRI enabled precise localization of dentate gyrus activitythat could not be resolved with the highest spatial resolution Nyquistacquisition.

2.8 Optimal Regularization Parameters for HSPARSE fMRI

The role of regularization parameters in fMRI reconstruction and theirsystematic optimization has been studied. As shown in FIG. 10, theglobal optimal range does not include the smallest λ₁ or λ₂ values (i.e.λ₁=5e⁻⁵ or λ₂=1e⁻⁹), which indicated neither utilizing only spatialregularization nor utilizing only temporal regularization can achievethe same high quality as the combined method. As shown in FIG. 10A, onlyutilizing the spatial regularization was not enough to eliminate thenoise-like aliasing artifact with a high acceleration factor of 5.3. Onanother hand, while temporal regularization alone can be used toreconstruct the image, the additional spatial regularization can furtherimprove the HRF amplitude and reduce the NRMSE (FIG. 10B). The commonoptimal parameters can be identified for distinct activity patterns andbackground images. Because the ground-truth data was not available forreal imaging situations, such generalizable optimal regularizationparameters were essential for the practical feasibility of the HSPARSEfMRI.

The b-SSFP sequence used for the current implementation of the HSPARSEfMRI requires two acquisitions to avoid banding. The b-SSFP has addedbenefits including significantly less spatial distortion thanconventional GRE acquisitions and high microvascular sensitivity.

2.9 Applications of HSPARSE fMRI

Although b-SSFP sequence was used for the current method'simplementation, the random under-sampling spiral acquisition can also beapplied to other sequences, such as GRE or spin-echo. In addition, theHSPARSE fMRI can be combined with other high resolution techniques suchas parallel imaging to further improve SNR and image resolution.Furthermore, the technique can be generally applied to different fieldstrengths, RF coils, and gradients, enabling wide application of HSPARSEfMRI.

The HSPARSE will also benefit a wide range of neural circuitry studies.For example, while the dentate gyrus may play a key role in patternseparation, Nyquist acquisition fMRI failed to test this hypothesis dueto insufficient spatial resolution. Similarly, the HSPARSE fMRI can alsoresolve cortical layer-specific activities for building corticalprocessing models. Given these questions and the need for higher spatialresolution throughout the scientific community, the improvement providedby the HSPARSE method offers an important tool for the advancement offMRI.

Example 2

3.1 GPU Based HSPARSE fMRI Reconstruction

The randomly under-sampled datasets were reconstructed with a modifiedgradient descent method (FIG. 4). However, this iterative process washighly computationally intensive, especially when the whole 4D datasetneeds to be reconstructed at the same time to fully exploit the temporalsparsity. To enable fast reconstruction, all NUFFT, inverse NUFFT(iNUFFT), matrix arithmetic, and sparsifying transform (DCT)computations were paralellized onto the GPU. Among all of the processingsteps, the NUFFT and iNUFFT were the most computationally intensive. Asshown in FIG. 3B, 3C, the NUFFT and iNUFFT convolution processes thatresample the k-space between Cartesian grids and non-uniformtrajectories can be significantly accelerated by thousands ofsimultaneous GPU threads. Compared to a CPU based HSPARSE fMRIreconstruction, the GPU based method achieves 34-fold overallimprovement in reconstruction speed (FIG. 17).

3.2 Evaluation of Robustness with Motion

The robustness of HSPARSE fMRI was investigated to motion, which was acommon source of signal degradation in fMRI. In the simulation, 25different sets of motion profiles with maximum absolute translations of1 to 5 pixels (0.21 to 1.05 mm) were separately added to the A3 phantom(FIG. 18A). The A3 phantom (FIG. 18C) was selected because itswell-defined activity boundary could best facilitate the evaluation ofmotion-induced fMRI artifacts. Based on measurements from in vivodatasets, smaller translations were applied in the z dimension and therotation angle of each axis was limited to within ±0.5 degrees. Afterthe phantoms with motion were reconstructed, they were motion correctedby the inverse Gauss-Newton motion correction algorithm.

FIG. 18B shows that when the amount of motion increases, the number offalse positive voxels increases in both the original and the HSPARSEreconstructed images, while the difference between the HSPARSE and theoriginal images was small. When the motion was larger than 3-pixels, themean F-value of the HSPARSE images decreased compared to the originalimages (FIG. 18C). When the motion was larger than 4-pixels, the HSPARSEimages had lower sensitivity compared to the original images (FIG. 18D).Therefore, while there were minimal differences even up to 5-voxelmotion, the HSPARSE fMRI method was robust to motion up to 3-pixels byall measures. The amount of motion in the high spatial resolution invivo experiments was then evaluated, during which the subjects' headswere fixed with teeth and ear bars. FIG. 18E shows that the maximumamount of translation was less than 2.5-pixels and rotation was lessthan 0.2 degrees in all three subjects, which indicated the HSPARSE fMRIreconstruction method was robust for the head-fixed experimental setup.

Example 3

Real-time fMRI (rtfMRI) has been applied in many fields such as thebrain machine interface, presurgical planning, and neuronal feedbackcontrol. However, it remains challenging to achieving highspatiotemporal resolution rtfMRI. This was because the resolutionimprovement results in a significantly high computational overhead. Inaddition, although many high-resolution techniques have been applied tothe rtfMRI, even higher resolution may be needed when investigatingfunctions of cortical layers and hippocampal sub-regions.

Here, compressed sensing (CS) was used to solve this problem. CSexploits the signal redundancy in certain domain and enables signalreconstruction from its under Nyquist rate sampled measurements. Due tothe natural massive redundancy in the MRI image, CS can be used in MRIto achieve higher imaging speed, signal to noise ratio (SNR), andspatial resolution. For the functional MRI (fMRI), CS can improve thespatiotemporal resolution, contrast to noise ratio (CNR), andsensitivity. Being an under-sampling method, CS can be implementedwithin the limit of available MRI scanner hardware. CS can also beintegrated with other techniques such as the parallel imaging to furtherimprove the resolution.

In aspects of embodiments of the current disclosure, a real-timehigh-resolution CS fMRI method was demonstrated, which can reconstruct a3D stack of variable density spiral sampled and 140×140×32 matrix sizedimage in less than 605 ms. With an optimized random stack of variabledensity spiral, highly incoherent sampling was achieved with anacceleration factor of 3.6 and 4-fold spatial resolution improvement.With this real-time high-resolution CS fMRI method, improved CNR, meanF-value and sensitivity with low false positive rate was also achieved.This method can also resolve layer specific activities that cannot beresolved with the highest spatial resolution Nyquist sampled image.

Materials and Methods 4.1 Randomized Stack of Variable Density Spiral

Incoherent measurement of k-space was required for CS reconstruction.Although random skipping phase encodings following a Gaussian-likedistribution in Cartesian imaging results in highly effective incoherentsampling, random skipping interleaved readouts in spiral imaging doesnot produce enough incoherency and temporal regularization was requiredfor eliminating the aliasing artifact. However, temporal regularizationrequires simultaneous reconstruction of whole/part of the fMRI dataset,which requires extremely high computational power. Furthermore, temporalregularization also introduces unavoidable temporal distortion.Therefore, a simple but effective spiral under-sampling strategy wasused for the real-time compressed sensing fMRI.

A trajectory design similar to the Variable Density Randomized Stack ofSpirals (VDR-SoS) technique was used for the real-time compressedsensing fMRI. VDR-SoS trajectory outperforms the standard stack ofspirals trajectory with a much higher acceleration factor and samplingincoherency. The reconstruction of the VDR-SoS sampled image was alsofaster than other 3D compressed sensing trajectories and produced asimilar reconstruction quality.

An Archimedean spiral trajectory can be described in the polarcoordinate as:

k=k _(r) e ^(jθ)  (5.1)

where k=k_(x)+ik_(y), r=√{square root over (k_(x) ²+k_(y) ²)} and

$\theta = {{\tan^{- 1}\left( \frac{k_{y}}{k_{x}} \right)}.}$

For spiral imaging, the change rate in the radial direction (Δk_(r))versus the change rate in the angular direction (Δθ) can be determinedby the effective FOV (FOV_(e)) in the x and y.

$\begin{matrix}{\frac{\Delta \; k_{r}}{\Delta\theta} = \frac{N}{2\pi \; {FOV}_{e}}} & (5.2)\end{matrix}$

where $N$ was the number of interleaves. By adjusting the effective FOV,different variable density spiral can be achieved. FIG. 24A, 24B and Eq.(5.3) show a design of a random stack of variable density spirals (VDS)trajectory with effective FOV following an exponential function:

$\begin{matrix}{{FOV}_{e} = {{FOV}_{0}{\exp\left( {- \left( \frac{k_{r}}{{ak}_{rmax}{\exp \left( {- \left( \frac{k_{r}}{{bk}_{zmax}} \right)^{2}} \right)}} \right)^{2}} \right)}}} & (5.3)\end{matrix}$

where FOV₀ was the desired field of view (e.g., 3.5 cm), k_(rmax) wasthe maximum value of k_(r) (1/(2×resolution in x and y)), k_(z) waslocation for each k_(z)-slice, and k_(zmax) was the number ofk_(z)-slices. The parameter a and b control the variance of theexponential function along k_(r) and k_(z) direction and determine theacceleration factor.

Following the effective FOV constraint (Eq.(5.3)), if the number ofinterleaves was maintained across different k_(z)-slice, the readoutduration would decrease when k_(z) increases. Therefore, readouts ondifferent k_(z)-slices would subject to different off-resonance effects.In order to avoid this, the number of interleaves on each k_(z)-slicewas designed to decrease with the increase of k_(z), and the number ofinterleaves was set to the integer that gave the closest number ofreadouts as the center k_(z)-slice. Because this technique also reducedthe total number of interleave, a high acceleration factor can beachieved.

To introduce higher incoherency into the sampling trajectory, the angleof each interleaf was randomly disturbed with a maximum angular changeof 0.05×π/N_(z). Because achieving the exact total number of desiredinterleaves for each 3D volume was not trivial for this trajectory, thetrajectory was first designed with a slightly larger total number ofinterleaves by adjusting the parameters a and b. Then sampling wasrandomly skipped on some of the interleaves on the outer k-spacefollowing a Gaussian distribution.

The random stack of VDS trajectory was then applied for the high spatialresolution fMRI. A fully-sampled stack of spirals trajectory with thehighest achievable spatial resolution was first designed, with 35×35×16mm field of view, 0.5×0.5×0.5 mm spatial resolution, and 70×70×32 matrixsize. There were 10 interleaves in each k_(z)-slice and 432 readouts ineach interleaf. A randomized stack of VDS trajectory was then designedwith same FOV but 4 times spatial resolution improvements in both the xand y dimension (i.e., 0.25×0.25×0.5 mm spatial resolution) compared tothe fully-sampled trajectory. Parameter a, b, and the number of readoutand interleaves were empirically picked based on the sparsity of theresultant point spread function (a=0.9, b=1.4). With these parameters,an acceleration factor of 3.6 was achieved compared to the fully-sampledhigh spatial resolution spiral sequence. The results show that thisparameter choice also produced highly incoherent sampling. Thefully-sampled and the random under-sampled trajectories were implementedinto a balanced steady state free precession sequence with TR/TE=9.237/2ms and 3s temporal resolution.

4.2 Reconstruction with the FISTA

The randomly under-sampled dataset was reconstructed in real-time usingan l1 Wavelet spatial regularization:

f(m)=1.2∥Fm−b∥ ₂ ²+λ∥Ψ_(s) m∥ ₁  (5.4)

where m was the image under-reconstruction, b was the raw k-spacesample, F was the non-uniform Fourier transform, and ω_(s) was the threedimensional Daubechies-4 discrete Wavelet transform (DWT).

This cost function was solved in real-time with the fast iterativeshrinkage thresholding algorithm (FISTA), which provides a highconvergence speed. In addition, because computation of the l1 normgradient was not required in FISTA, the computational complexity of theFISTA in each iteration was much lower than the gradient descent basedmethods.

Because FISTA only supports ∥x∥₁ instead of ∥Ψx∥₁ regularization, thecost function was modified to reconstruct the image in the Waveletdomain.

f(m)=1.2∥F⋅ _(s) *w−b∥ ₂ ² +λ∥w∥ ₁  (5.5)

where Ψ_(s)* was the inverse Wavelet transform. After reconstructionwith Algorithm 1 (FIG. 19), the final image can be obtained by takingthe inverse DWT of w.

Algorithm 1, shown in FIG. 19 was implemented on a Graphical ProcessingUnit platform. Several repeatedly computations such as the non-uniformFFT (NUFFT), inverse NUFFT, DWT and inverse DWT were carefullyoptimized. For the NUFFT, a pre-sorting algorithm was implemented. Acustom build workstation was used for the real-time reconstruction withIntel quad-core 2.66 GHz CPU, Nvidia 2048 cores CUDA GPU and 16 GB CPUmemory.

4.3 fMRI Analysis

The general linear model analysis was performed with five gamma basisfor the real-time reconstructed datasets. An F test was then performedwith active voxels recognized as those having an F-value larger than4.12 (P<0.001).

A phantom with 30 dB SNR was first designed to identify the optimal CSregularization parameter and evaluate the real-time reconstructionquality. Six-cycle 20s-on-40s-off canonical statistical parametric map(SPM) hemodynamic response function (HRF) were added to the phantom witha spatial distribution simulating in vivo experiments. The peak HRFamplitude of the fMRI signal was set to be 5% modulation, resulting intoa CNR of 1.28. Six different regularization parameters were tested(1e⁻², 5e⁻³, 1e⁻³, 5e⁻⁴, 1e⁻⁴, 5e⁻⁵). The quality of the reconstructedfMRI images was then evaluated by comparing the contrast to noise ratio(CNR), mean F-value, peak hemodynamic response function (HRF) amplitude,sensitivity, and false positive rate to the corresponding metrics of theground-truth image. Normalized root mean squared error between thereconstructed and the ground-truth images were also compared. Thesensitivity was measured by the number of active voxels within theoriginally designed active region over the total number of voxels of thedesigned active region. The false positive rate was measured by thenumber of active voxels within the first and the second voxel perimeterlayers of the designed active region over the total number of voxelswithin the first and the second voxel perimeter layers.

Results 5.1 Real-Time CS Reconstruction

The convergence speed of the FISTA MRI reconstruction was first comparedwith the widely used conjugate gradient descend MRI reconstructionmethod (CG) (Lustig et al., 2007). As can be seen in FIG. 20A, the FISTAMRI reconstruction method showed a much faster convergence speed (about30 iterations) than the CG method. FISTA MRI reconstruction alsoresulted in a much lower cost.

As shown in FIG. 20B, 20C, the computation speed of the FISTAreconstruction was measured on a GPU. With the GPU optimization, theNUFFT took 5.74 ms and the inverse NUFFT took 9.75 ms. The highlyoptimized DWT and inverse DWT only took 0.59 ms and 0.61 msrespectively. The overall FISTA reconstruction (30 iterations) took603.09 ms, which was only 20% of the duration between consecutive fMRIimages. The real-time inverse gauss newton motion correction (IGNMC) andcoherence analysis took less than 5.39 ms and 4.42 ms. The totalprocessing time of the real-time system was less than 620 ms. This highspeed enabled future integration of advanced analysis algorithms intothe real-time system such as the brain state classification. Moreover,this fast processing speed also indicated the real-time system can beused for high temporal resolution CS fMRI.

5.2 Reconstructed Image Quality

FIG. 21 shows the comparison of the zero-filling reconstructed datasetwith the ground-truth image. As can be seen, the designed random stackof VDS trajectory showed significantly high incoherence in the sampling,i.e., the aliasing artifact was highly noise like. The real-time CSreconstructed images was then evaluated with the optimal regularizationparameter (see next section, =1e-3). The FISTA algorithm wassuccessfully removed the noise like aliasing artifacts compared to thezero-filling reconstruction. Although the CS reconstructed image wassmoother than the ground-truth image due to the spatial regularization,the CS reconstructed image showed that it can still detect layerspecific activity that cannot be resolved by using the highest spatialresolution Nyquist sampled image.

5.3 Optimal Regularization Parameter

The optimal regularization parameter was identified by scanning througha range of potential optimal parameters. As shown in FIG. 22, when theregularization was strict (such as when λ=1e⁻²), the image was smoothand DWT thresholding artifact became observable. When the regularizationwas loose (such as when λ=5e⁻⁵), the noise level increased.

The CNR, mean F-value, peak HRF amplitude, NRMSE, sensitivity and FPRwas then compared across the regularization parameters. The optimalregularization parameters were identified as the intersect of theparameters that gave top 50% CNR, mean F-value, sensitivity, and bottom50% NRMSE and false positive rate. λ=1e⁻³ and 5e⁻⁴ was then identifiedas the optimal regularization parameter for the reconstruction. Withλ=1e⁻³, improvement was achieved at 1.47, 2.28, 1.23 times in the CNR,mean F-value and sensitivity respectively, peak HRF amplitude of 3.82%,NRMSE of 0.21 and low FPR of 0.058.

5.4 High Spatial Resolution

The spatial resolution of the real-time CS fMRI was also tested withphantoms containing layer specific activity. As shown in FIG. 23A, twointerleaved layers consisted of signals with distinct peak HRF amplitudewere designed in the first phantom. The real-time high-resolution CSfMRI clearly resolved the two layers while the highest spatialresolution Nyquist sampled image completely failed. The raw HRFs colorlabeled by their corresponding original layer index also confirm thereal-time high-resolution CS fMRI outperformed the highest spatialresolution Nyquist sampled method. As shown in FIG. 23B, another twoHRFs with distinct latencies were added into the phantom with the sameinterleaved pattern. It was shown that the real-time high-resolution CSfMRI also successfully resolved the latency between layers, while thehighest spatial resolution Nyquist sampled image failed.

5.5 CS fMRI Method

A real-time high-resolution CS fMRI method that can reconstruct a 3Dvolumetric image (140×140×32 matrix size) in less than 605 ms has beendemonstrated. Based on FISTA method, the reconstruction showed muchfaster convergence speed and accuracy than the widely used conjugategradient method. With the designed high spatial resolution random stackof VDS, high incoherent sampling was achieved. Improvement by 1.47,2.28, 1.23 times in the CNR, mean F-value, sensitivity and low FPR of0.058 after the reconstruction was also achieved. This method can alsoaccurately detect layer specific activity which cannot be resolved bythe highest spatial resolution Nyquist acquisition.

5.6 Application of the Real-Time High-Resolution CS fMRI

Many applications can be benefited from the real-time CS fMRI with the 4times spatial resolution improvement disclosed herein. For example,regions involved in seizure form a highly dynamic network that couldvary across subjects and time. With this real-time high-resolution CSfMRI system, seizure propagation can be monitored and tiny but criticalbrain regions in the network, such as the dentate gyrus, can also beclosely studied with the high-resolution provided by the method.

With the reconstruction speed as fast as 605 ms per 3D image, thereal-time CS fMRI method can be applied to high temporal resolutionimaging with little modification of the scanning trajectory. Similar tothe demonstrated high spatial resolution trajectory, a high temporalresolution trajectory can be designed with Eq. 5.3 by reducing thenumber of interleaves compared to the standard resolution Nyquist image.With the high temporal resolution image, finer fMRI temporal dynamicscan be captured and the brain network connectivity can be investigatedin real-time.

Furthermore, the real-time high-resolution CS fMRI method can also becombined with the parallel imaging techniques to further improve theimage CNR, sensitivity, and to achieve a higher acceleration factor. Insummary, the real-time high-resolution CS fMRI of the present disclosureoffers an important tool to the advancement of fMRI technology.

While the present invention has been described with reference to thespecific embodiments thereof, it should be understood by those skilledin the art that various changes may be made and equivalents may besubstituted without departing from the true spirit and scope of theinvention. In addition, many modifications may be made to adapt aparticular situation, material, composition of matter, process, processstep or steps, to the objective, spirit and scope of the presentinvention. All such modifications are intended to be within the scope ofthe claims appended hereto.

1.-20. (canceled)
 21. A method comprising: acquiring image data of atarget area in a subject using a variable density spiral (VDS)trajectory, wherein the VDS trajectory includes a stack of VDStrajectories, wherein a change in a radial direction versus a change ina rate in an angular direction of the VDS trajectories is determined atleast in part by a number of interleaves and an effective field of view,wherein the field of view follows an exponential function and an anglebetween each interleaf of the number of interleaves is varied; andproducing an image of the target area in the subject based on the imagedata.
 22. The method of claim 21, wherein producing the image comprisesreconstructing the image data using a wavelet spatial regularization.23. The method of claim 22, wherein the wavelet spatial regularizationincludes a three dimensional Dubechies-4 discrete wavelet transform. 24.The method of claim 23, wherein the image is produced by taking aninverse of the discrete wavelet transform.
 25. The method of claim 21,wherein the number of interleaves decreases as k_(z) increases in ak-space of the image data.
 26. The method of claim 21, wherein at leastsome of the interleaves on an outer portion of a k-space of the imagedata are randomly skipped.
 27. The method of claim 26, wherein theinterleaves on the outer portion of the k-space are randomly skippedfollowing a Gaussian distribution.
 28. A method comprising: acquiringimage data of a target area in a subject using a stack ofmulti-interleaf variable density spiral (VDS) trajectories, wherein atotal number of interleaves follows a Laplacian distribution and acenter of a k-space is more densely sampled than an outer portion of thek-space; and producing an image of the target area in the subject basedon the image data.
 29. The method of claim 28 further comprising:acquiring two phase-cycled images of the target area; and combining thetwo phased-cycled images by maximum intensity projection.
 30. The methodof claim 28, wherein producing the image comprises iterativelyreconstructing the image from the image data using an L1 regularizedcost function.
 31. The method of claim 30, wherein reconstructingcomprises regularizing a temporal domain and a spatial domain by adiscrete cosine transform.
 32. The method of claim 30, wherein the L1regularized cost function is solved by a gradient descent method. 33.The method of claim 28, wherein producing the image comprisesreconstructing the image from the image data using one or moreregularization parameters.
 34. The method of claim 33, wherein the oneor more regularization parameters includes at least one of a contrast tonoise ratio, an active volume within a designed active region of theimage data, mean F statistic value, normalized root mean squared error,or peak hemodynamic response function amplitude.
 35. A functionalmagnetic resonance imaging (fMRI) system, the system comprising: areceiver configured to acquire image data of a target area in a subjectusing a variable density spiral (VDS) trajectory, wherein the VDStrajectory includes a stack of VDS trajectories, wherein a change in aradial direction versus a change in a rate in an angular direction ofthe VDS trajectories is determined by a number of interleaves and aneffective field of view, wherein the field of view follows anexponential function and an angle between each interleaf of the numberof interleaves is varied; and a processor configured to produce an imageof the target area in the subject based on the acquired image data. 36.The system of claim 35, further comprising a coil configured to apply abalanced steady state free precession (b-SSFP) sequence to the targetarea in the subject.
 37. The system of claim 35, wherein the processoris further configured to analyze the image data using a fast iterativeshrinkage thresholding algorithm (FISTA).
 38. The system of claim 35,wherein the processor is configured to produce the image duringacquisition of the image data by the receiver or immediately afteracquisition of the image data by the receiver.
 39. The system of claim35, further comprising a coil configured to apply an excitation waveformto the target area in the subject.
 40. The system of claim 35, whereinthe processor is further configured to analyze the image data using asparsifying transform.